Financial Instability, Credit Cyclesand Monetary Policy∗

Christian Heebøll-ChristensenDepartment of Economics, University of Copenhagen

Incomplete first draft: please do not distribute or cite this paper without permission from the author.

April 15, 2011

Abstract

This paper is focused on the relations between monetary policy, credit growthand financial instability. From the two theoretical views on financial instability;the money view and the credit view, the paper does a cointegrated VAR analysisof essential macroeconomic mechanisms between house prices, money and creditgrowth, and the related influence of monetary and structural policy. The analysisuses US data from 1984 to 2010; especially focused on the period preceding theglobal financial crisis starting in 2008. Consistent with the credit view, the em-pirical results suggest persistent mutually reinforcing cycles of house prices andleverage. Importantly, a prolonged boom period from the middle of the 1990s -with an excess house price inflation from 2003 - seems to be terminated with thebust of the global financial crisis in the beginning of 2008. While the busts of thesecycles are related to the financial crisis periods, the initializations of the boomsseem both theoretically and historically related to exogenous shocks of financialderegulation and innovation. Monetary policy has an important, though minor,influence on these cycles through the short term interest rate. The results alsosuggest a stabile money demand-supply balance, in accordance with the moneyview. Here, the expansionary monetary policy preceding the global financial crisisseems to have resulted in a period of excess money supply. However, the influenceon house prices from this channel is found relatively small. Generally, the analysissuggests a direct focus on leverage and asset prices cycles, both from the view ofmonetary policy makers and especially from a structural policy point of view.

Keywords: financial instability; house prices bubbles; credit view; moneyview; cointegrated VAR model; impulse response analysis.

∗I have received valuable comments and suggestions from Michael Bergman

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1 IntroductionRapid credit growth together with both financial deregulation and longer periods ofexpansionary monetary policy are seen in most OECD countries preceding the globalfinancial crisis. Similarly holds for earlier local financial crises as shown by Reinhartand Rogoff (2009). In the literature, financial crises are often considered caused bymechanisms of financial instability, i.e. asset price bubbles build through a precedingperiod, yet with no consensus on the underlying macroeconomic mechanisms. Thispaper is focused on two of the major contradictory theoretical views on this matter; themoney view and the credit view. In favor for the money view, the lines are drawn byCongdon (2005, p. 12):1

"Theories that relates asset price booms to the volume of credit or to banklending, rather than the quantity of money are misconceived."

This view builds on the optimal portfolio idea of Friedman (1956) assuming perfectfinancial markets. The monetary boom-bust theory by Meltzer (1995a) states that assetprice bubbles generally are caused by too fast growing quantity of money. Hence, amoney supply-demand balance should be the main focus of policy makers. Contrary,credit growth is purely seen as a natural manifestation of riskless shifts of purchasingpower over time and various states of the economy.

The credit view retaliates as argued by Cooper (2008, p. 53):

"Money also means debt growth, and it is the debt that causes the financialinstability."

This view rests on the assumption of imperfect financial markets. Originating from theideas of Keynes (1936), the financial instability hypothesis by Minsky (1993) suggestsa general instability of credit economies in which financial instability is build throughmulti horizontal cycles of risk appetite, credit growth and asset price bubbles. Morerecent theories point to similar symptoms resulting from limited liabilities in credit con-tract, e.g. the asset boom-bust theory by Allen and Gale (2000) and the model of creditcycles by Kiyotaki and Moore (1997). Using asset price bubbles as collateral, creditgives the possibility not just for shifting but also for creation of purchasing power, unre-lated to any real increase in value or voluntary saving in the economy. Hence, in contrastto the money view, credit growth is seen as the central factor of financial instability. Atone point theories of the credit view differ somewhat, however; the initial cause of creditcycles. In line with the money view, some argue for the effects of too expansionary mon-etary policy, while others argue for structural policy changes, i.e. financial liberalization

1The terminology of the money and credit view is also used in Kakes (2000) and originates partlyfrom Schumpeter (1961). This will continually be used in this paper.

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and innovation. Notwithstanding, according to the credit view, policy makers should bestrictly focused on the evolution of credit and asset price cycles.

The empirical litterateur analyzing these mechanisms is multifaceted. One type ofempirical analysis - typically done in a cross country setup - considers the interactionbetween real economic and financial cycle characteristics. Claessens et al. (2010) findthat especially credit and house price cycles have a large impact on the amplitude anddepth of real economic booms and busts. Similar for early warning systems, creditaggregates in combination with asset prices are typically found superior to the monetaryaggregates in predicting financial crises, giving support to the credit view (Borio andLowe, 2004; Gerdesmeier et al., 2009; Misina and Tkacz, 2009).

An alternative to cross-country studies is to use a time series approach. Helblinget al. (2010) estimate a global factor-augmented vector autoregressive (VAR) model forthe G-7 countries. Here, they find significant effects of credit shocks on real globalbusiness cycles, also in support for the credit view.2 Other types of models allow forchanges in the macroeconomic mechanisms of financial instability, argued by the assetboom-bust model, among others. Kaufmann and Valderrama (2007) estimate a Markovswitching VAR model on Euro area and US data including equity prices and creditgrowth. They find evidence of a two-stage regime both in the Euro area and in the US,switching quite frequently between a demand and supply driven credit market. Here,credit shocks are found particular important during demand driven markets. Using anon-linear threshold VAR and a smooth transition model respectively, Atanasova (2003)and Juselius and Kim (2011) similar find variations in the effects of different exogenousshocks, particular depended on the conditions in the credit market. Generally, theseresults also support the credit view. Somewhat in support for the money view Goodhartand Hofmann (2008) - also in several earlier papers with coauthors - by use of panelVAR model find multidirectional links between house prices and monetary variable,even strongly so from 1985 and when house prices are booming. Also using a panelmodel to determine asset prices evolution, Adalid and Detken (2007) identifies excesscredit and liquidity shock by the use of a VAR model. Here, they find strong supportfor the money view; that excess liquidity shocks clearly are superior to credit shocksin determining asset price boom and busts. Similar is found globally for the OECDcountries by Belke and Orth (2007).

Further, analysis using cointegration allows for specific focus on possible long runfinancial instability building mechanisms. These analysis are, however, quite limitedin number. Greiber and Setzer (2007) includes credit as a robustness check in a coin-tegrated VAR model studying the effect of monetary policy on house prices in the USand Euro area. They find that the quantity of money rather that credit is important forhouse prices, in support for the money view. Giese and Tuxen (2007) does a similar

2Similar results are found by Eickmeier and Hofmann (2010), Gilchrist and Zakrajsek (2010), andMeeks (2009) considering only shocks to credit spreads.

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analysis on global perspective finding a surge in global liquidity from the early 2001with positive effect on global house prices.

The purpose of this paper is to analyze macroeconomic mechanisms of financial in-stability within a cointegrated VAR model using US data . Based on the two theoreticalviews on financial instability; the money view and the credit view, the analysis focuseson possible reinforcing behavior between house price bubbles, money and credit growthand the related influence of monetary and structural policy. The paper contributes to theliterature in several ways. Firstly, unlike many earlier studies, I estimate a cointegratedVAR model on levels variables including, among others, a real money measure, the realamount of credit, and real house prices. By allowing for cointegration, I am able to testand compare the long run relations and effect derived from the two theoretical views.Secondly, I explicitly model the persistent leverage and asset price cycles observed overthe last decades, consistent with theoretical literature on unstable financial markets andhistorical regimes of financial regulation. In the end, I analyze the fundamental drivingmechanisms by the use of generalized and structural impulse response functions, bothin regard to shocks to the endogenous variables and shocks to exogenous deterministicterms associated with financial deregulation etc.

My main result is that there is evidence of persistent and mutually reinforcing cyclesof leverage and real house prices related to historical periods of financial deregulationand crises, consistent with the credit view. [MORE HERE]

The rest of this paper is organized as follows: section 2 discusses the econometricmethodology, section 3 the theoretical consideration and section 4 introduces the data.Further the empirical analysis is initiated in section 5 with a specification of the modelwhereas section 6 presents the empirical results. Section 7 gives a robustness check ofthe results and section 8 discuss and concludes.

2 MethodologyThe analysis uses an I(1) cointegrated VAR model (CVAR) analyzing both the cointe-gration structure of the vector error correction representation (VECM) and the impulseresponses functions (IR) of the vector moving average representation (VMA). In the fol-lowing I will generally use the terminology and notation of Juselius (2007). When ana-lyzing financial instability the CVAR model is suitable for several reasons. In general,the model uses a multiple dimensions formulation. This allows for a theory consis-tent macroeconomic interpretation as well as a general-to-specific estimation process.Further, the VECM representation allows for specific analysis of the theory consistentmedium and long run relations of financial instability accumulation. In the end, theVMA representation facilitates analysis of theory consistent financial instability drivingshocks in the economy.

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2.1 The VECM representationThe CVAR model can be written on several forms. When cointegration relations are notaccounted for the model can be written as a VAR(k) model with k autocorrelation lags:

xt =k

∑i=1

Πixt−i +ΦDt + εt (1)

where xt is a vector of p endogenous variables at time t = 1, 2 , ...,T , the Πis arep× p matrices for the i = 1, . . . ,k lags of the model, and Dt is a vector of deterministiccomponents, e.g. a constant, a trend, impulse and shift dummies, with coefficientsvector Φ. ε is a p× 1 vector of multivariate normally distributed residuals; with avariance-covariance matrix Ω. ε ∼ Np(0,Ω)

The vector of endogenous variables comprises the logarithm of real house prices (h),real credit (k), real money (m) and real GDP (y), a nominal three month t-bill rate (i3m),the nominal own-rate on money (iown), and a credit risk spread (cs). Hence, xt is a sevendimensional vector given by:3

x = [h, k, m, y, i3m, iown, cs]′. (2)

The CVAR model facilitates a characterization of the data variation into short, mediumand long run by the order of integration; I(0), I(1) and I(2) respectively. To analyze coin-tegration relations, (1) with k = 2 can be written on VECM(1) representation:

∆xt = Πxt−1 +Γ1∆xt−1 +ΦDt + εt (3)

where Π = −(I−Π1−Π2) and Γ1 = −Π2. Assuming for now that the level variablesin (2) are at most I(1), the medium run CI(1,1) cointegration hypothesis is equivalentto a reduced rank hypothesis of the Π-matrix. Importantly here, only the Π-matrix hasreduced rank and all terms in (3) are stationary. The reduced rank hypothesis can beverified by the I(1) trace test and the number of near unit roots in the characteristicpolynomial of the Π-matrix (unit roots), among others. If the Π-matrix has a rankof r < p and, hence, s1 = p− r unit root the model contains r cointegration relationsand s1 I(1) common stochastic trends. To analyze the cointegration relations, the Π-matrix can appropriately be written as the matrix product Π = αβ ′, where α and β

are p× r matrixes. Here, the β -matrix describes the structure of the r medium runrelations while the α-matrix determines the corresponding, possible error correctionor overshooting tendencies of each variable. To give an economic interpretation, thecointegration relations need to be identified on the basis of theoretical consistent testson the β -matrix.

3These variables will be further motivated by theoretical consideration in section 3 and specified inthe data section 4.

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As will be found in the empirical analysis, some of the variables in (2) do evolverelatively persistent. In an I(1) CVAR model, as such persistency is incompatible withan I(1) process, this will show up as signs of I(2) or broken deterministic trends withseveral implications. First of all, not only the Π-matrix has reduced rank but now alsothe Γ-matrix.4 Hereby the number of unit roots in the Π-matrix, p− r, both includeI(1) and I(2) common stochastic trends, i.e. p− r = s1 + s2 where si is the numberof I(i) trends. Secondly, in (3) all terms except the residual will in general be I(1),hence, β ′xt does not only include purely medium run CI(1,1) cointegration relationsbut also CI(2,1) and even long run CI(2,2) cointegration relations. To see this, considerestimating the cointegration relations using the so-called R-model where the regressandand cointegration relations in (3) are concentrated out for short run effects ∆xt−1 andthe deterministic terms Dt (Juselius, 2007, p. 116, 292):

∆xt = B1(∆xt−1 +Dt)+R0t (4)xt = B2(∆xt−1 +Dt)+R1t . (5)

Using the residuals of these regressions, the R-form is given by:

R0t = αβ′R1t + εt . (6)

When xt ∼ I(2) and thus ∆xt ∼ I(1), it can be found from (4) and (5) that R0t ∼ I(0) andR1t ∼ I(2).5 Inserting (5) into (6) shows the following:

R0t︸︷︷︸I(0)

= αβ′( xt−1︸︷︷︸

I(2)

−B2 (∆xt−1 +Dt)︸ ︷︷ ︸I(1)

)+ εt (7)

= α(

β′xt−1−ω

′(∆xt−1 +Dt)︸ ︷︷ ︸I(0)

)+ εt

where ω = β ′B2. As the left hand side of (7) is stationary the same will be the case forthe right hand side, i.e. β ′i R1t ∼ I(0). For each of the i = 1, . . . ,r cointegration relationsthere are two ways to achieve this:

1. cointegration directly to stationarity: β ′i xt−1 ∼ I(0) and ωi = 0 or

2. polynomial cointegration: β ′i xt−1 ∼ I(1) cointegrate with ω ′i (∆xt−1 +Dt)∼ I(1),i.e. β ′i xt−1−ω ′i (∆xt−1 +Dt)∼ I(0) with ω ′i 6= 0.

4More specifically, when xt ∼ I(2) a reduced rank restriction is imposed on the transformed Γ matrix:α ′⊥Γβ⊥ = ξ η ′, where ξ and η are (p− r)× s1 matrixes.

5The I(1) trends in ∆xt cancel when regressing on the I(1) variable; ∆xt−1, but the I(2) trends in xtcannot cancel when regressing on an I(1) variable.

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The polynomial cointegration relations involves the first differences of the I(2) trends,hence, given r > s2, it is possible to identify the model having s2 polynomial cointegra-tion relations and r− s2 directly stationarity relations.6 Hence, I(2) trends in the datawill show up as substantial deviations between some of the cointegration relations onR-form, βiRt , and their corresponding cointegration relations on standard form, βixt .7

To determine the number of I(2) and I(1) trends, the I(2) trace test and number of nearsingle and double unit roots are also applicable.

The overall problem when data includes such persistent variables is that the I(1)CVAR gives an unsatisfactory data description, e.g. miss polynomial cointegration re-lations.8 There are, however, different way to explicitly deal with this in the I(1) CVARmodel, two of which I will apply in the empirical analysis. One possibility, appropriatewhen the persistence seems related to a few plausibly exogenous long run trend breaks,is to include deterministic terms. In (7) the first difference of the possible I(2) trends aremodeled by unrestricted shifts. Broken trends restricted to the cointegration relationsare also included but should turn insignificant if the trend breaks cancel in the coin-tegration, i.e. are similar for all variables. In the ideal case, when only the long runtrend breaks are modeled by the deterministic components, the number of cointegrationrelations should not change.9 One advantage of this method is that it facilitates explicitanalysis and economic interpretation of the exogenous included deterministic compo-nents. An important drawback, however, is that there is no clear way to test the structureand exact dates of the these deterministic components within the CVAR model.

Another method is related to the so-called nominal-to-real transformation (Kong-sted, 2005). In monetary CVAR models it is often found that consumer prices and thenominal quantity of money are near I(2). Under certain condition, among others thatthe two I(2) variable share the same common I(2) trend, an I(1) CVAR model can give acomplete data description by the means of a homogenous transformation. In monetaryCVAR models, instead of including consumer prices and the nominal quantity of moneythe model is transformed to including the real quantity of money and the consumer priceinflations. The test of whether this transformation is truly valid can only be done in theI(2) CVAR model. If it is valid, the rank of the Π-matrix should be unaltered. Thistransformation will serve as a robustness check of my empirical results.

6When r ≤ s2 it is more difficult to determine the minimum amount of polynomial cointegration.7Note, β ′i R0 ∼ I(0) for all cointegration relations while βix∼ I(1) for s2 cointegration relations.8Note here; the I(2) CVAR model is nested as a restricted version of the I(1) CVAR model, hence, the

problem only lies in the data description.9The condition for this to hold is that the deterministic modeling of each I(2) trend does not remove a

corresponding I(1) trend, i.e. the first difference of the I(2) trend.

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2.2 The VMA representation and IR functionsTo analyze how credit and monetary policy shocks transmit through the system I willalso use IR analysis on the VMA representation. From (3) the model can be written onreduced VMA representation:

xt =Ct

∑s=1

(εs +ΦDs)+C∗(L)(εt +ΦDt)+ X0 (8)

where X0 is a vector of initial values, C∗(L)=Σtj=0C∗j L

j determines the transitory effectsfrom the stationary part of the the process, while the C-matrix determines the permanentimpact of shocks to the residuals, i.e. the common stochastic trends. The C-matrix can- as a parallel to the partition of the medium run structure in the Π-matrix - be writtenas C = β⊥α ′⊥ where β⊥ = β⊥(α

′⊥Γβ⊥)

−1. Assuming possible I(2) trends are adequatelymodeled, the α ′⊥-matrix determines the construction of the s1 common stochastic trendsin relation to the residuals, while the β⊥-matrix determines how each variable reacts tothe common stochastic trends (Juselius, 2007, p. 255). When I(2) trends are determin-istically modeled long run trends will result from the accumulation of unrestricted shiftin Ds.

The model in (8) is on reduced form; only conditioning on predetermined variables,i.e. variables at time t − 1, t − 2, . . . Hereby the model might systematically omit toexplain important simultaneous effects in the data, showing up as large non-diagonalelements in the variance-covariance matrix. IR analysis, however, demands additionalassumptions entailing a diagonal variance-covariance matrix. In this paper I will con-sider two types of IR functions; structural (SIR) and generalized (GIR) IR functions. Thestructural form is focused on keeping a clear economic interpretation. Correlated residu-als are in contrary to theory consistent structural shocks hitting the economy exogenousand independently. The p residuals of the reduced form model are therefore related top ”underlying” linear independent structural shocks (Juselius, 2007, p. 278):10

ut =

(ul,tus,t

)= Bεt (9)

where ut is a vector of p− r permanent and r transitory structural shocks, ul,t and us,trespectively. B is a p× p restriction matrix. Inserting (9) into (8) the model can bewritten on structural VMA representation:11

xt = C([

∑ti=1 ul,i

∑ti=1 us,i

]+ΦDs

)+C∗(L)B−1

([ul,tus,t

]+ΦDt

)+ X0 (10)

10Here I use the terminology used in Juselius (2007, ch. 15) which is somewhat different from others;e.g. the B−1, C, C∗(L) and ε in Juselius (2007) are equivalent to B, Ξ, Ξ∗(L) og u respectively in Lütkepohland Krätzig (2004, ch. 4).

11Likewise the VECM representation can be written on structural form: (3) multiplied through by thematrix B; B∆xt = BΓ∆xt−1 +Bαβ ′xt−1 +BΦDt +ut , where ut = Bεt , ut ∼ N(0,Σ), Σ = BΩB′.

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where C = β⊥α ′⊥B−1 and C∗(L)B−1 determines the permanent transitory effects re-spectively. While allowing for current effects, post multiplying by B−1 introduces p · pnew coefficients in the model. To achieve just-identification therefore requires addi-tional p · p restrictions on the B and/or C-matrix. In the literature there are several waysof doing this (Lütkepohl and Krätzig, 2004, p. 163-171). In accordance with macro-economic theory, these restrictions will be imposed so that the structural shocks satisfythree conditions; i) linearly independence (ut ∼ N(0, Ip)), ii) they are separated intop− r permanent and r transitory shocks and iii) the variables have a clear causal chaingiving rise to clear economic interpretation.12 In the identified representation (10), theSIR functions determines the dynamics of a given variable when the model is hit by agiven structural shocks (ui,t). At horizon n (n periods after the shock) this is given by:

SIR(n,δ ,B) = Et(xt+n|ul,i,t = δ ,B)−Et(xt+n|B) (11)

where Et is the expectations operator given the identified model in (10) and δ will bescaled as one standard deviation. The interpretation of (11) is in line with economictheory. It is, however, important to note that the additional restrictions, ordering and in-terpretations of the structural form cannot be tested, which is a critical and controversialpoint of the structural from (Juselius 2007, p. 232, 287; Lütkepohl and Krätzig 2004, p.195).

One way of dealing with these issues is by doing GIR analysis (Pesaran, 1998). GIRfunctions describe the dynamics of a given variable when the model is hit by a shock to areduced form residual, εi,t , when all current effects described by the historical variance-covariance matrix are accounted for.13 At horizon n this is given by:

GIR(n,δ ,Ωt1) = Et(xt+n|εi,t = δ ,Ωt1)−Et(xt+n|Ωt1) (12)

where Et is the expectations operator given the identified model in (8). In additionto shocking residuals in (12), one could also analyze the IR function when shockingdeterministic components, replacing εi,t with a specific component of Dt (Blanchard andPerotti, 2002). Likewise replacing xt+n with βxt+n, (12) could also be used to analyzehow the cointegration relations are effected by different types shocks, which is way toconsider the link between the VMA and VECM representations. GIR functions are notquite consistent with macro economic theory, which makes them difficult to interpret.

12By (p+ 1)p/2 restrictions on the B-matrix the first condition (ut ∼ N(0, Ip)) is assured. Here (p−1)p/2 restrictions on the non-diagonal elements of the B-matrix assures independence, while another prestrictions on the diagonal of the B-matrix assures a standardized distribution. In sum these restrictionare; B′=[α ′Ω−1α−1/2α ′Ω−1,(α ′⊥Ωα⊥)

1/2α ′⊥]. The second condition is assured by another (p−r)r zero-restrictions on the last r columns of C-matrix, while the third condition is assured by the remainingrestrictions (Juselius, 2007, p. 278-279).

13GIR functions allows for simultaneous effects on all other residuals within period t. Hereby thevariable of the impulsed/shocked residual can be considered the first variable in the causal chain, i.e.would give the same result as if it was the first variable in a Cholesky decomposition.

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For example, consider a monetary shock as a policy reaction to a certain economicevolution. Here, one cannot separate the effect of the monetary policy shock from theeffect of the economic evolution leading this policy reaction.

3 Theoretical considerationsThe theoretical literature on financial instability can be separated into different viewsrelated to different macroeconomic schools. Here I will focus on the two contradictoryviews; the money view and the credit view. These will be addressed in relation tothe empirical methodology and summarized in a theory consistent CVAR hypotheticalframework.

3.1 The money viewThe money view is related to the monetarist optimal portfolio idea, restated by Friedman(1956). It relies on assumptions of perfect financial markets and the theorem of capitalstructure irrelevance (Modigliani and Miller, 1958). Hereby the quantity of money isseen as the central important variable in the economy.

Meltzer (1995b) model the money view explicitly in relation to financial instabil-ity. The model, on the one hand, assumes certain agent preferences for consumptionand holdings of liquidity, assets and bonds, dependent on the current market situation,agents expectations and risk aversion etc. On the other hand, there are opportunity costsof holding liquidity etc., among others given by the market interest rate. Jointly, agentshereby seek to hold a portfolio balances where marginal utilities equal marginal costs;on a macroeconomic scale adding up to a money supply-demand balance. In this frame-work, financial instability is initialized by a positive monetary policy shocks, causing asituation of excess money supply or, equivalent, excess liquidity of agents’ portfolios.14

In this situation agents seek to rebalance to their optimal portfolios, meaning selling offliquidity, with the result of higher consumption, lower interest rates and booming assetprices. This is the central mechanism of the model. Additional, as an accelerating effect,excess money supply is assumed to transmit into high market liquidity, essential mean-ing low asset price fluctuations.14 Consolidating a vicious circle, this seemingly positivestabile market situation lead agents to feel less uncertain about the future, hence, theirliquidity preferences fall causing even more excess liquidity etc.

14Macroeconomic liquidity is defined as the total amount of liquids asset usable for transactions andpayment to creditors. It differs from market liquidity, which is defined as the ability of a market toabsorb temporary supply and demand shocks without significant price changes. The two are related in thefollowing way: under high market liquidity assets invested can easily be converted into macroeconomicliquidity without significant loss of value (Allen and Gale, 2007, p. 52).

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Importantly, these mechanisms are centered around the money supply-demand bal-ance, which according to the money view should be the main focus of monetary poli-cymakers.15 In the empirical literature this relation is presented in different ways, in-cluding different measures of money, opportunity costs of holding money, transactionmotives and agent expectations. In the empirical model presented in (2) I will focus onthe following money relation (constants are not reported):16

mt = β11yt−β12(i3m,t− iown,t)+β12cst (13)

where all β1is are expected positive; the quantity of money (mt) should increase whenthe opportunity cost of holding money (i3m,t− iown,t) decrease or as a result of the trans-action motive when the economic activity increases (yt). Uncertainty in the financialmarkets, represented by an increased credit risk spread (cst), may introduce a flight toequality effect where investors seek to hold more money (Cook and Choi, 2007; Greiberand Lemke, 2005).

Excess liquidity is represented by money supply exceeding the level of money de-mand given by the right hand side of (13). The relevant hypothetical question is whethersuch a money supply-demand relation exists, whether excess liquidity affects real assetprices positively and, in addition, leads economic growth and lowered uncertainty. Moreso, in the model presented in (2) I expect to find two common stochastic, medium rundriving trends in the economy; one being real economic supply shocks and the other onebeing shocks to the real money supply. Here, asset price bubbles are expected purelydriven by shocks to the real money supply.

3.2 The credit viewThe credit view, on the other hand, assumes imperfect financial markets, motivated bythe periods of financial deregulation before the Great Depression and after the 1970s.Capital and financial market structures - including the relative amount of credit in theeconomy - are hereby key determinants in relation to financial instability.

A central theory is the post-keynesian financial instability hypothesis by Minsky(1993).17 The theory assumes a capitalist dynamic economy where economic growthis generated by ever new profit giving investments. Investors are more or less fundedby borrowing in the credit markets, involving complex structures of credit contracts.In this framework, financial instability is driven by a growing disequivalence between

15Meltzer (1995b) show that in the situation where the monetary policymakers control the interest rateand not the money supply the financial instability scenario is even more likely to occur.

16This money demand relation can be found from the money in the utility function CCAPM (Greiberand Lemke, 2005). A similar relation could be found from other monetarist theories, e.g. the Beaumol-Tobin model (Carstensen, 2003).

17This theory builds, among others, on the credit view by Schumpeter (1961, ch. 3), the debt-deflationtheory by Fisher (1933), and especially the general theory by Keynes (1936, ch. 11-12).

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the structure of payment commitments on credit contracts and the future expected andrealized cash flow of investments. This is partly build over what Minsky calls a basiccycle where the confidence, risk appetite and exposure of both investors and lenders es-calates.18 On top of this, working over several basic cycles, he assumes longer so-calledsuper cycles where not only market agents but also policy markers adopt over optimisticconfidence in the stability of the economic system (Ferri and Minsky, 1992).19 As theconfidence and risk appetite of market agents increase concurrent with regulatory au-thorities easing the market discipline the whole economy becomes increasingly fragile.This is what Minsky (1993) defines as financial instability, seen as growing leveragecollateralized by asset prices bubbles.

More recent theories incorporate similar mechanisms into a general equilibriumframework, e.g. the asset boom-bust theory by Allen and Gale (2000) and the modelof credit cycles by Kiyotaki and Moore (1997). These assume limited liability in creditcontracts whereby credit growth has a risk shifting effect from borrowers to lenders.More leverage and lower cost of capital, i.e. a lover so-called debt obligation ration,means less net worth and less liability of lenders. Ultimately, the less liability moretendency of lender for overly risk seeking investments, driving vicious circles of assetprice bubbles and credit growth.

Theories differ somewhat on whether this instability is self-generated by the eco-nomic system or - e.g. as argued by Ferri and Minsky (1992)’s super cycles - initializedby exogenous shocks of various kinds:20 changes in the financial market structures,regulations etc.; overoptimistic expectations of new economic regimes; or overlay ex-pansionary monetary or fiscal policy. Assuming different starting points, periods ofaccumulating financial instability have many facets, but according to the credit view theinstability accelerating mechanism is always the same.

From the view of a policymaker, the theory recommends a non-thwarting so-called”leaning against the wind” monetary and credit policy, aimed to contain the inherentinstable financial markets. In Mehrling (2011)’s interpretation, by setting the short terminterest rate central banks dictate the balance between elasticity and discipline in thefinancial markets. During booms, involving proclivity for excess elasticity in the finan-cial markets, central banks should tighten the monetary policy. In contrast, during buststhe tendency is towards excess market discipline why central banks should attempt toease the market conditions.

18Here I refer to the evolution in the economy including a shift in the number of the three types offinancing units; hedge, speculative and ponzi financial units Minsky (1993)

19This is what Ferri and Minsky (1992) calls thwarting institutions. Results of such over optimisticthoughts are e.g. "The Great Moderation", "The New Economy" and "The East Asian Miracle". Similarmechanisms are argued by George Soros and Mehrling (2011).

20The financial accelerator explains credit growth as a result of monetary policy shocks affecting thenet worth of banks, households and firms and thereby banks’ willingness to supply credit. The assetboom-bust theory, on the other hand, relates credit growth to financial liberalization and innovation.

12

Similar to the money view, the credit view argues for at least one financial instabilityrelation. From the financial instability hypothesis, financial instability is build overcycles of two different horizons. From the model in (2), I first of all expect the followinglong run relation, to be compared with a Minsky super cycle:

ht = β21(kt− yt)−β23i3m (14)

Here again, all β2is are expected positive. The right hand side shows the debt obligationratio. Increasing overall leverage of the economy, kt − yt , or decreasing cost of capital,i3m, is associated with decrease in liabilities, more risk shifting between house ownersand their banks, and rising real house prices. The central hypothetical questions of thecredit view in relation to (14) are whether such a relation exists and whether houseprices are positively affected by an excess debt obligation ratio. Secondly, I also expectto find indications of a medium run relation between agents’ risk appetite and businesscycle fluctuations, to be compared to Minsky’s basic cycles:21

yt− yPt = −β31cst . (15)

Here y−yP indicates the output gab (deviation between actual and potential output). β31is expected positive as high economic growth (yt) should lead high risk appetite (−cst).

Given the model in (2), I expect to find three long run driving trends in the economy;real economic supply shocks, credit supply shocks, and monetary policy shocks to theshort term interest rate. Minsky (1993)’s super cycle hypothesis further argues thatcredit supply shocks will have a longer horizon then other shock in the economy, similarto the difference in the horizon of (15) compared to (14). However, the central questionsin relation to financial instability building mechanisms are whether credit shocks andshocks to the interest rate has positive effects on real house prices.

3.3 Additional combining theoretical considerationsConsidering the model in (2) from the two theoretical views gives two quite differentideas about the macroeconomic mechanisms of financial instability. Conditioned on theexact interpretation, these two views may or may not exclude each other completely. Forexample, even thought the credit view states that credit is the central driver of financialinstability there might still be a stabile money demand relation. Some theories even

21This relation could also be argued by theories of irrational pro-cyclical expectations (Borio et al.,2001). Business cycle theory predicts that economic booms are followed by times of low economicgrowth, why economic booms should rationally result in low expectations of future economic growth -counter cyclical expectations. Theories of irrational pro-cyclical expectations, as well as the financialinstability hypothesis, argues for the opposite.

13

suggest that both types of mechanisms are at work, though, more or less relevant atdifferent points in time.22

The model in (2) includes seven variables, expectedly either two or three commonstochastic trends and, hence, either five or four equilibrium relations. Hence, in additionto the relations in (13) - (15), other relation should be expected as well: e.g. a relation foraggregate demand (AD), the expectations hypothesis and/or a policy rule of the centralbank. In addition, one might suggest a relation between real money, real credit andinterest rates. Viewing money as current purchasing power, credit is as a way to movepurchasing power ahead in time at the cost of interest payments.23

4 DataThe empirical analysis is based on monthly US data from 1984:04 to 2010:09. Themodel includes real credit in levels, given as real debt outstanding to nonfinancial sec-tors (k). For the nominal to real calculation I generally use CPI. The quantity of moneyis modeled as the real M2M (m), i.e. M2 excluding small time deposits, as done byGreiber and Setzer (2007), among others.24 Real economic activity is modeled by realGDP (y). For asset prices I include real house prices (h), motivated by several earlierfindings that financial crises typically are associated with bust of house price bubbles(Claessens et al., 2010).25 Along the lines of monetary VAR models, I include the nom-inal own rate on money (iown) and a three month t-bill rate (i3m) (Carstensen, 2003).26

In the end, to model risk appetite of financial markets, I include a credit spread betweenMoodys BBB rated corporate bonds and a ten year government bond (cs) as done byCook and Choi (2007) and Greiber and Lemke (2005), among others. House prices,credit and GDP are Chow-Lin interpolated from quarterly to monthly data using prox-ies of higher frequency. The details and data sources are shown in appendix A, and theseven variables of the model are shown in levels and first differences in figure 1 and 2respectively.

22E.g., the boom-bust model suggest that the credit view is dominating the boom period while themoney view - or at least quite similar effects - are dominating the bust period, see Allen and Gale (2000)vs. Franklin and Douglas (1998).

23This is consistent with The Theory of Money and Credit by Ludwig von Mises (1912); also arguedby Mehrling (2011).

24The M2M measure is, among others, used in order to avoid the problems of ”the missing money ofthe 1990s”. Especially in the beginning of the 1990s, because of the financial innovation and deregulation,it seems that small time deposits have been substituted by different types of mutual fund products out sideof the standard money measure, M2 (Carlson and Keen, 1996).

25On this point the results differ somewhat, however. In an early warning analysis Gerdesmeier (2010)finds that, in combination with credit, stock prices represent the best performing financial variable anddominate house prices.

26The federal funds rate would perhaps be more correct as a monetary policy measure. It, however,suffers from very discreet changes, incompatible with the normality assumption.

14

Figure 1: The variables of the model in levelsh

1990 2000 20105.0

5.5

h k

1990 2000 2010

9.0

9.5

10.0 k m

1990 2000 2010

8.0

8.5

m y

1990 2000 2010

9.0

9.5y

i_own

1990 2000 2010

2.5

5.0i_own i_3m

1990 2000 2010

5

10 i_3m cs

1990 2000 2010

2

4

6 cs

Note: The shaded areas indicate the crisis periods.

Figure 2: The variables of the model in first differencesDh MA(Dh)

1990 2000 2010

0.00

0.02 Dh MA(Dh) Dk MA(Dk)

1990 2000 2010

−0.01

0.00

0.01

0.02Dk MA(Dk) Dm MA(Dm)

1990 2000 2010

0.00

0.02

Dm MA(Dm) Dy MA(Dy)

1990 2000 2010

−0.01

0.00

0.01

0.02Dy MA(Dy)

Di_own MA(Di_own)

1990 2000 2010−0.5

0.0

Di_own MA(Di_own) Di_3m MA(Di_3m)

1990 2000 2010

−0.5

0.5 Di_3m MA(Di_3m) Dcs MA(Dcs)

1990 2000 2010

0

1

Dcs MA(Dcs)

Note: i) The shaded areas indicate the crisis periods. ii) MA indicates 11 month moving averages.

4.1 The sample period and economic regimesIn the CVAR model it is important to have a relatively constant economic regime and,hence, probable constant relations between the model variables (Juselius, 2007, p. 149).However, this might not be exactly true for the period preceding the global financial.The starting point of the sample, the early 1980s, marks the beginning of ”The Great

15

Moderation”, among others, meaning a more stabile economic and monetary policyregime.27 More so, it marks the revival of a belief in market efficiency, both in the USand in most other OECD countries. This tendency brought substantial financial deregu-lations and innovations, possibly affecting the economic regime throughout the sampleperiod. According to Chomsisengphet and Pennington-Cross (2006) and Temkin et al.(2002), when considering credit and especially mortgages, three reforms are of particu-lar importance, primary related to the Reagan deregulation period of 1981 - 1989:

1. The Depository Institution Deregulation and Monetary Control Act, 1980, remov-ing a deposit interest rate ceiling, originally established back in the 1930s.

2. The Alternative Mortgage Transaction Parity Act, 1982, generally allowing forvariable interest and balloon payment on loans.

3. The Tax Reform Act, 1986, allowing for special tax deduction of mortgages in-terest payments.

Later, especially one reform also had an important influence (Gorton, 2008):

4. The Gramm-Leach-Bliley Act, 1999, which took down barriers to competitionbetween traditional banks, investment banks, and insurance companies.

These reforms, in together with similar reforms in other OECD countries, removedsubstantial credit rationing and market constrains and induced more competition, scoopfor financial innovations and general development of national and international capitalmarkets. As one important example, the subprime mortgages eased the credit conditionssubstantially by introducing a new segment of house owners to the market (Gorton,2008; Miller and Stiglitz, 2010). Subprime lending is seen from the beginning of the1990s, with a rapid growth of 760% for home purchase from 1993 to 1998.

Looking through the data, theses structural changes in the economic regime intogether with known historical crisis periods seem to coincide with somewhat syn-chronous trend breaks in real credit and real house prices (seen as shaded/non-shadedareas of the figures). The financial deregulations of the Reagan period, 1981 - 1989, isassociated with high credit growth and notable house price inflation. In the beginningof the 1990s follows the savings and loan crisis (S&L) inducing a short period of al-most zero credit growth and low house price inflation.28 From the middle of the 1990sthe situation again changes to a fast growing tendency, possibly caused by new innova-tions, among others, the subprime mortgages. This tendency continues until the globalfinancial crisis, also believed to be the end of ”The Great Moderation”. Hereafter both

27In the period before, "The Great Inflation", the quantity of money was both the target and the primarymonetary policy instrument: the period of monetary (M3) targeting, 1980-1983.

28The saving and loan crisis refer to the failure of 747 savings and loan associations in the US, in thelate 1980s and early 1990s. The total cost of was $87.9 billion for resolving.

16

real house prices and real credit fall rapidly. Looking at the other variables the picture ismore unclear. Generally, however, the crises periods, i.e. the S&L in the early 1990s, theDot-com in the early 2000s, and the global financial crisis, all seem associated with loweconomic growth, higher credit spreads and decreasing interest rates. The real moneysupply seems less systematically related.

4.2 A firsthand view of the theoretical relationsTurning to the theoretical considerations, figure 3 shows a firsthand view of the expectedrelations.

Figure 3: Data viewed from a theoretical perspective(A) Velocity and opp. costs (SM) (B) House prices and leverage (C) Output gap

Money velocity (y−m) Opportunity cost (i_3m−i_own)

1990 2000 2010

0.8

1.0

Money velocity (y−m) Opportunity cost (i_3m−i_own)

Real house prices (h) Leverage (k−y)

1990 2000 20105.0

5.5

Real house prices (h) Leverage (k−y)

Excess liq.[b(i_3m−i_own)−(m−y)] House price inflation (MA(Dh))

1990 2000 2010

−2

0

2 Excess liq.[b(i_3m−i_own)−(m−y)] House price inflation (MA(Dh)) House price to leverage [h−(k−y))]

Interest expenses (i_3m)

1990 2000 2010

5.0

5.1

5.2House price to leverage [h−(k−y))] Interest expenses (i_3m)

Real credit (k) Real money (m)

1990 2000 2010

9.0

9.5

10.0Real credit (k) Real money (m)

Interest expenses (i_3m) Excess money to credit (m−b*k)

1990 2000 2010

−0.1

0.0

0.1

0.2 Interest expenses (i_3m) Excess money to credit (m−b*k)

Potential output (trend) Actual output (y)

1990 2000 2010

9.00

9.25

9.50Potential output (trend) Actual output (y)

Output gap (y−bt) Inverse credit spread (−cs)

1990 2000 2010

−0.05

0.00

0.05 Output gap (y−bt) Inverse credit spread (−cs)

Money velocity (y−m) Opportunity cost (i_3m−i_own)

1990 2000 2010

0.8

1.0

Money velocity (y−m) Opportunity cost (i_3m−i_own)

Real house prices (h) Leverage (k−y)

1990 2000 20105.0

5.5

Real house prices (h) Leverage (k−y)

Excess liq.[b(i_3m−i_own)−(m−y)] House price inflation (MA(Dh))

1990 2000 2010

−2

0

2 Excess liq.[b(i_3m−i_own)−(m−y)] House price inflation (MA(Dh)) House price to leverage [h−(k−y))]

Interest expenses (i_3m)

1990 2000 2010

5.0

5.1

5.2House price to leverage [h−(k−y))] Interest expenses (i_3m)

Real credit (k) Real money (m)

1990 2000 2010

9.0

9.5

10.0Real credit (k) Real money (m)

Interest expenses (i_3m) Excess money to credit (m−b*k)

1990 2000 2010

−0.1

0.0

0.1

0.2 Interest expenses (i_3m) Excess money to credit (m−b*k)

Potential output (trend) Actual output (y)

1990 2000 2010

9.00

9.25

9.50Potential output (trend) Actual output (y)

Output gap (y−bt) Inverse credit spread (−cs)

1990 2000 2010

−0.05

0.00

0.05 Output gap (y−bt) Inverse credit spread (−cs)

Money velocity (y−m) Opportunity cost (i_3m−i_own)

1990 2000 2010

0.8

1.0

Money velocity (y−m) Opportunity cost (i_3m−i_own)

Real house prices (h) Leverage (k−y)

1990 2000 20105.0

5.5

Real house prices (h) Leverage (k−y)

Excess liq.[b(i_3m−i_own)−(m−y)] House price inflation (MA(Dh))

1990 2000 2010

−2

0

2 Excess liq.[b(i_3m−i_own)−(m−y)] House price inflation (MA(Dh)) House price to leverage [h−(k−y))]

Interest expenses (i_3m)

1990 2000 2010

5.0

5.1

5.2House price to leverage [h−(k−y))] Interest expenses (i_3m)

Real credit (k) Real money (m)

1990 2000 2010

9.0

9.5

10.0Real credit (k) Real money (m)

Interest expenses (i_3m) Excess money to credit (m−b*k)

1990 2000 2010

−0.1

0.0

0.1

0.2 Interest expenses (i_3m) Excess money to credit (m−b*k)

Potential output (trend) Actual output (y)

1990 2000 2010

9.00

9.25

9.50Potential output (trend) Actual output (y)

Output gap (y−bt) Inverse credit spread (−cs)

1990 2000 2010

−0.05

0.00

0.05 Output gap (y−bt) Inverse credit spread (−cs)

(E) Excess liquidity and houseprices inflation (SM)

(F) House price to leverage andinterest expenses (SM)

(G) Output gap and invertedcredit spread (SM)

Money velocity (y−m) Opportunity cost (i_3m−i_own)

1990 2000 2010

0.8

1.0

Money velocity (y−m) Opportunity cost (i_3m−i_own)

Real house prices (h) Leverage (k−y)

1990 2000 20105.0

5.5

Real house prices (h) Leverage (k−y)

Excess liq.[b(i_3m−i_own)−(m−y)] House price inflation (MA(Dh))

1990 2000 2010

−2

0

2 Excess liq.[b(i_3m−i_own)−(m−y)] House price inflation (MA(Dh)) House price to leverage [h−(k−y))]

Interest expenses (i_3m)

1990 2000 2010

5.0

5.1

5.2House price to leverage [h−(k−y))] Interest expenses (i_3m)

Real credit (k) Real money (m)

1990 2000 2010

9.0

9.5

10.0Real credit (k) Real money (m)

Interest expenses (i_3m) Excess money to credit (m−b*k)

1990 2000 2010

−0.1

0.0

0.1

0.2 Interest expenses (i_3m) Excess money to credit (m−b*k)

Potential output (trend) Actual output (y)

1990 2000 2010

9.00

9.25

9.50Potential output (trend) Actual output (y)

Output gap (y−bt) Inverse credit spread (−cs)

1990 2000 2010

−0.05

0.00

0.05 Output gap (y−bt) Inverse credit spread (−cs)

Money velocity (y−m) Opportunity cost (i_3m−i_own)

1990 2000 2010

0.8

1.0

Money velocity (y−m) Opportunity cost (i_3m−i_own)

Real house prices (h) Leverage (k−y)

1990 2000 20105.0

5.5

Real house prices (h) Leverage (k−y)

Excess liq.[b(i_3m−i_own)−(m−y)] House price inflation (MA(Dh))

1990 2000 2010

−2

0

2 Excess liq.[b(i_3m−i_own)−(m−y)] House price inflation (MA(Dh)) House price to leverage [h−(k−y))]

Interest expenses (i_3m)

1990 2000 2010

5.0

5.1

5.2House price to leverage [h−(k−y))] Interest expenses (i_3m)

Real credit (k) Real money (m)

1990 2000 2010

9.0

9.5

10.0Real credit (k) Real money (m)

Interest expenses (i_3m) Excess money to credit (m−b*k)

1990 2000 2010

−0.1

0.0

0.1

0.2 Interest expenses (i_3m) Excess money to credit (m−b*k)

Potential output (trend) Actual output (y)

1990 2000 2010

9.00

9.25

9.50Potential output (trend) Actual output (y)

Output gap (y−bt) Inverse credit spread (−cs)

1990 2000 2010

−0.05

0.00

0.05 Output gap (y−bt) Inverse credit spread (−cs)

Money velocity (y−m) Opportunity cost (i_3m−i_own)

1990 2000 2010

0.8

1.0

Money velocity (y−m) Opportunity cost (i_3m−i_own)

Real house prices (h) Leverage (k−y)

1990 2000 20105.0

5.5

Real house prices (h) Leverage (k−y)

Excess liq.[b(i_3m−i_own)−(m−y)] House price inflation (MA(Dh))

1990 2000 2010

−2

0

2 Excess liq.[b(i_3m−i_own)−(m−y)] House price inflation (MA(Dh)) House price to leverage [h−(k−y))]

Interest expenses (i_3m)

1990 2000 2010

5.0

5.1

5.2House price to leverage [h−(k−y))] Interest expenses (i_3m)

Real credit (k) Real money (m)

1990 2000 2010

9.0

9.5

10.0Real credit (k) Real money (m)

Interest expenses (i_3m) Excess money to credit (m−b*k)

1990 2000 2010

−0.1

0.0

0.1

0.2 Interest expenses (i_3m) Excess money to credit (m−b*k)

Potential output (trend) Actual output (y)

1990 2000 2010

9.00

9.25

9.50Potential output (trend) Actual output (y)

Output gap (y−bt) Inverse credit spread (−cs)

1990 2000 2010

−0.05

0.00

0.05 Output gap (y−bt) Inverse credit spread (−cs)

Note: Some graphs are scaled to match means and ranges (SM).

Figure 3.A shows the composition of a money supply-demand relation (13). Thisimplies an excess money supply which is further shown in figure 3.E together withhouse price inflation. The period preceding the global financial crisis, 2004 - 2008, wasassociated with excess money supply, meaning expansionary monetary policy. How-ever, house price inflation seems to lead this imbalance, in contrast to the expected. Inrelation to the credit view, figure 3.B shows the long term cycle relation (14) betweenleverage and asset prices. In general, leverage and house prices seem quite related. Also

17

in this relation there seems to be imbalance from 2003 and onwards where the growth inleverage slows down while house prices inflation accelerate even further. One possibleexplanation is the effect of varying interest rates, meaning that one should consider thedebt obligation ratio. Figure 3.F shows the components of the debt obligation ratio butdoes leaves a question mark about this explanation. In the end, figure 3.C and 3.G givean indication of Minsky’s basic cycle, i.e. increasing risk appetite over the a standardbusiness cycle. The output gab and risk appetite (inverse credit spread) especially seemrelated through the last decade.

5 Model specificationIn this section I specify the model. This involves determining the number of lags anddeterministic components and test for cointegration rank. Additionally, I also test forparameter constancy.

5.1 General specificationTable 1 shows the information criteria and the LM test for lag length determination forthe model including the seven variables, a constant and a restricted linear trend. Thetests all indicates a lags length of k = 2.

Table 1: Test for lag lengthModel k SC H-Q LM(k)VAR(3) 3 -64.586 -66.887 0.002VAR(2) 2 -65.0768 -67.035 0.047VAR(1) 1 -64.082 -65.698 0.000

Note: SC: Schwarz Criterion, H-Q: Hannan-Quinn Criterion, LM(k): LM autocorrelation test of order k.

Further, I check for extraordinary large residuals in the model. For the follow-ing dates at least one residual violates the normality assumption;29 1984:11, 1987:10,1992:07, 2001:05, 2001:09, 2005:09, 2005:11, 2006:09, 2008:02, 2008:05, 2008:08,2008:10, 2008:11. These dates can all, more or less, be related to historical events asshown in figure 2. Including unrestricted impulse dummies for these dates, I further testthe normality assumption. As seen from table 3 the multivariate Doornik-Hansen testrejects general normality of the model. When considering the multivariate LM tests thisnon-normality seems related to some degree of general heteroscedasticity. Consideringthe univariate tests, especially the interest rate variables seem to have some over kurtosis(>3) and ARCH effect. As this should not make a problem for the interpretation andestimation process the model seems fairly well specified (Juselius, 2007, p. 75, 110).

29In accordance with the literature, these are residuals larger than 3.5 standard deviations.

18

Table 2: Economic outliers/eventsPeriod Economic interpretation1984:11 A large expansionary monetary policy shock.*1987:10 Financial market crash.1992:07 Influence of the early 1990s S&L crisis period.2001:05, 2001:09 Sudden economic slowdown and the reactions to the terror attack of 9/11.2005:09 - 2006:09 Jumps ind the inflation rate affecting the nominal-to-real calculations.2008:02 - 2008:11 Influence of the global financial crisis.

Note: *In two month, 1984:09 - 1984:11, the effective federal funds rate went from 11.23% to 8.92%with a contagious effect on the short terms interest rates in the model.

Table 3: Miss specification testMultivariate testsNormality Autocorrelation Heteroscedasticity (ARCH)

DH:χ2(14) LB(82):χ2(3920) LM(2):χ2(49) LM(4):χ2(49) LM(2):χ2(1568) LM(4):χ2(3136)245.259[0.000]

4548.003[0.000]

68.910[0.032]

46.091[0.592]

2017.736[0.000]

4074.379[0.000]

Univariate tests ∆h ∆k ∆m ∆y ∆i3m ∆iown ∆csNormality (p-value) 0.099 0.007 0.005 0.000 0.000 0.000 0.000Skewness 0.200 0.341 0.130 0.163 -0.082 0.006 0.266Kurtosis 3.453 3.789 3.873 4.099 4.366 4.971 5.954ARCH(2) (p-value) 0.136 0.044 0.000 0.723 0.005 0.172 0.000

Note: DH is a Doornik-Hansen test, LB is the Ljung-Box, LM(k) is a LM test for autocorrelation andARCH of order k. Clear signs of non-normality is marked bold.

5.2 Cointegration rank and stochastic and deterministic trendsDue to the observed persistency in the data, I shall start out by checking for I(2) trendsin the model. Figure C.1 in appendix B shows the first four possible cointegration rela-tions of the model on standard form and R-form, i.e. (6) where the short run dynamic isconcentrated out. As seen, especially the first relation gives some indication of polyno-mial cointegration, hence, I(2) trends in the data. Further table C.1 in appendix B showsthe largest unrestricted root calculation and the I(2) trace test. Since an unrestricted rootabout 0.9 is broadly accepted as different from unity, they both indicate a rank of r = 4,s1 = 2 I(1) trends and s2 = 2 I(2) trends.

To give an indication of the origin of the I(2) trends table 4 shows univariate Dickey-Fuller tests of all variables in first differences. Only for real house prices and real creditthe univariate hypothesis of an I(2) trend cannot be rejected, even on a 1% significantlevel. This is in accordance with previous presumptions from the data section 4. Here,structural policy changes in together with known historical crisis periods also seemedassociated with trend breaks in both real credit and real house prices. Similar is sug-gested by the theoretical considerations given in section 3, i.e. that credit cycles can

19

be initialized by different types of shocks, among others, exogenous shocks of financialderegulation and innovation. These findings generally point to the sense of determinis-tic modeling of the near I(2)-ness, as explained in section 2.1. This will be done in thefollowing, while an alternative specification will be analyzed as a robustness check insection 7.

Table 4: Dickey-Fuller test for the variables in first difference∆h ∆k ∆m ∆y ∆iown ∆i3M ∆cs

DF −2.69 −1.74 −3.50∗∗ −3.97∗∗ −3.65∗∗ −4.05∗∗ −5.73∗∗

Note: i) Significant level and critical values: 1%≈−3.45∗∗, 5%≈−2.87∗, 10%≈−2.57. ii) Allregressions include 11 lags.

As mentioned, there is no appropriate test for determining the structure and datesof deterministic components within the CVAR model. Besides considering historicaleconomic events, I therefore run a simple univariate two stage Markov switching processon real credit growth, only including a switching constant. Table C.2 and figure C.2in the appendix B show the regime classification and the actual and fitted values ofthe regression. In general, I find three regime shifts; 1990:01, 1993:07 and 2008:01plus a short temporary regime over the middle of the global financial crises; 2008:08 -2008:12. These are explicitly shown in table 5 together with their possible economicinterpretation, as discussed previously in the data section 4.30

Table 5: Outline of economic periodsPeriod Economic interpretation1984:04 - 1990:01 The Reagan deregulation period and the beginning of "The Grate Moderation".1990:02 - 1993:07 The early 1990s S&L crisis period.1993:08 - 2008:01 The period of global financial deregulation and innovation.2008:01 - 2010:09 The global financial crisis and the end of "The Grate Moderation".

To account for these, I include three unrestricted shifts and test for broken trendsrestricted to the cointegration space, bt90:01, bt93:07, bt08:01, in 1990:01, 1993:07 and2008:01. The tests are shown in table 6. Generally, when considering the bartlett cor-rected tests, I cannot reject the exclusion of any trend breaks in the cointegration re-lations. However, as the bartlett-correction is done on the basis of the model with nodeterministic terms, it might be miss leading. Considering the extreme case, the non-bartlett corrected tests totally reject joint exclusion of all broken trend, somewhat con-sistent with the previous result of two I(2) trends in the data. The tests suggest a jointlyexclusion of bt08:01,bt93:07 or perhaps, on the very edge of rejection, bt08:01,bt90:01. Con-sidering the focus on the effect of structural policy changes, I will here, in spite of the

30The historical events and statistical regime shifts do not conjunct exactly, especially regarding theReagan period which ended in January, 1989.

20

lower p-value of the bartlett corrected tests, jointly exclude bt08:01,bt90:01 from the coin-tegration space.

Table 6: Test for exclusion of the restricted broken trendsUnivariate t-test Multivariate LR-test

bt90:01 bt93:07 bt08:01 bt90:01,bt93:07 bt90:01,bt08:01 bt93:07,bt08:01 All btp-value 0.121 0.564 0.072 0.121(0.005) 0.285(0.032) 0.615(0.209) 0.147(0.004)

Note: In the multivariate tests, the big figures are bartlett corrected while the figures in sub-parastichiesare non-bartlett corrected. The correction factor, 1.697, is calculated on the basis of the model with nodeterministic terms.

Further, to account for the temporary regime during the financial crises, 2008:08- 2008:12, I also include a restricted shift in 2008:08, s08:08. This marks one of thebiggest shocks to the US economy in recent history; the beginning of the second face ofthe financial crisis with the crash of Lehman Brothers.31 Hereby, bt93:07 and s08:08 areindividually found significant with p-values 0.026 and 0.031, and jointly with a p-valueof 0.024.

Table C.3 in appendix B shows the unrestricted root calculation and the I(2) trace testof the model with all deterministic terms included.32 Both now seems to indicate no I(2)trends in the model or at least only small tendencies for I(2). Hence, the I(2) trends seemadequately modeled by the deterministic terms. However, the criteria are not consistentabout the cointegration rank. The unrestricted root calculations still indicates a rank ofr = 4 while the trace test indicates a rank of r = 5. A higher cointegration rank can,as mentioned in the methodology section 2.1, result when not only the I(2) but also therelated I(1) trends are modeled by the included deterministic terms.33 Turning to thetheoretical considerations, the choice of a rang of r = 4 or r = 5 is in accordance witheither the credit view or the money view respectively (see section 3). To allow for thericher data structure, allowing for a stochastic trends related to the financial and creditmarkets, I will set a rank of r = 4.

31The American investment bank Lehman Brothers filed for bankruptcy protection September 15,2008. The filing marked the largest bankruptcy in the entire U.S. history. Probably because of relatedfinancial tumult the shock to the model is found in the month preceding the bankruptcy protection.

32Note, when the model includes unrestricted shift dummies, the cointegration relation on R-form andnormal form will be quite different by construction and, hence, will not necessarily indicate I(2) trends inthe data.

33In a model including an I(2) trend, ∑∑ε1i, but no explicit corresponding I(1) trend term, ∑ε1i, if theI(2) trend is modeled by deterministic terms both the I(2) and I(1) trend will disappear. Hence, s1 + s2will decrease while r = p− s1 + s2 will increase.

21

5.3 Constancy of the parameter regimeWith a fully specified model, I test for the constancy of the parameters, as a last checkfor miss specifications. In figure C.3 and C.4 in appendix I show the test for a constantlog-likelihood and β -matrix. As seen, the β -matrix is generally quite constant, at least inthe R-model excluding the short run dynamics. Considering the log-likelihood, however,the model as a whole dose not exhibit constant parameters, which might indicate someproblems related to the α-matrix. As seen, this inconstancy arise in the early to mid1990s, in accordance with the discussion of difference financial regimes . Exactly whichrelation these problems relate to demands a specification of the β -matrix. I concludethat at least the cointegration structure seems quite robust.

6 Empirical findingsIn this section I test, identify and analyze the model; first the medium and long runstructure of the VECM representation and later the GIR and SIR functions of the VMArepresentation.

6.1 The VECM representationTo analyze the model on VECM representation the cointegration structure has to beidentified. This will be done by tests in accordance with the theoretical considerationsand economic questions presented in section 3. Individual non-identifying tests for therelations (13) - (15) in short gives the following results, as marked A - C in summarytable D.1 in appendix C:

A. First of all, a money demand relation (13) including s08:08 is accepted with a p-value of 0.166. In the final identified model, the money-output elasticity, β11, isfound quite close to unity and a unit restriction on β11 is also found borderlineaccepted in the non-identifying tests.

B. Further, the super Minsky cycle relation (14) including s08:08 is accepted with ap-value of 0.265. Here, the house price-leverage elasticity, β21, is also found closeto unity, and a unit restriction is accepted with a p-value of 0.138. When includedin this relation, the bt93:07 is found insignificant with a t-value of -0.607, meaningthat it cancels in the relation. The is not the case for a general trend.

C. The basic Minsky cycle relation (15) is, however, not found significant in themodel. Even including s08:08 the restriction has a p-value of 0.019. Alternatively,when replacing real GDP with real house prices and including bt93:07 the relationis found significant with a p-value of 0.704.

22

In total, the model is identified with a p-value of 0.033 using the three restriction typesA - C plus an AD type restriction, as shown in table 7 below.

Table 7: The identified cointegration structure of the VECM representationh k m y iown i3M cs s08:08 bt93:07 t

β1 - - 1.00−

−1.00−

−0.13(−15.66)

0.13(15.66)

−4.52(−7.13)

- - -

β2 1.00−

−1.00−

- 1.00−

- 0.06(9.65)

- 0.11(3.57)

- 0.00(5.58)

β3 1.00−

- - - - - 11.34(7.57)

- −0.00(−6.9)

0.00(0.36)

β4 - - - 1.00−

- −0.04(−9.79)

- −0.09(−3.07)

- −0.00(−34.75)

∆h ∆k ∆m ∆y ∆iown ∆i3M ∆csα1 −0.01

(−1.70)−0.02(−2.86)

−0.06(−7.98)

0.02(2.80)

0.32(4.28)

−0.18(−0.52)

0.00(−0.94)

α2 −0.01(−2.17)

0.03(4.23)

0.02(2.59)

−0.03(−4.03)

−0.19(−2.61)

0.77(2.30)

0.01(1.90)

α3 −0.01(−4.95)

−0.02(−4.81)

−0.02(−4.67)

−0.01(−1.76)

0.05(1.29)

−0.11(−0.65)

−0.01(−4.78)

α4 −0.01(−2.10)

0.02(2.14)

−0.04(−3.86)

−0.01(−1.32)

−0.04(−0.41)

1.67(3.93)

0.01(1.87)

Note: i) P-values are shown in the parenthesis. ii) Significant coefficients are marked bold. iii)Significant error correcting coefficients are marked red ind the α-matrix.

Interpreting the model, the first relation, β1, is much in line with what is expected ofa money demand relation:

β1 : m− y = −0.13 (i3m− iown) +4.52 cs (16)

The inverse money velocity, m− y, is negatively related to the opportunity cost of hold-ing money, i3m− iown. More os, there seems to be a flight to equality effect, i.e. whenthe risk aversion, cs, increases agents prefer to hold more money. In the non-identifyingtest a positive shift, s08:08, in the inverse money velocity is found significant, in line withthe non-standard monetary policy measures used by the Fed to increase the macroeco-nomic and market liquidity during the global financial crisis. This shift is, however,not found significant in the identified model. Turning to the α-coefficients, it is seenthat house prices seem unrelated to the excess liquidity in this relation. Considering thenon-identifying test shown in table D.1 in appendix C, this result is not quite consis-tent. A possible interpretation is that the significants of β1 in relation to house prices isdominated by the other relations in the identified system in table 7. Real money, GDPand the own rate on money error corrects to the relation as expected. Hence, taken fromthese results monetary policy at least has some influence on the real economy thoughthis channel.

The second relation, β2, is in line with the theoretical scenario of (14). Only, it alsoincludes a general trend and the shift i 2008:08:

β2 : h = (k− y)−0.06 i3m−0.11 s08:08−0.00 t (17)

23

The broken trends - both bt93:07 and bt90:01,bt08:01 jointly excluded in section 5.2 - allcancel in the relation. This means that the observed long run cycles are common for realhouse prices and the debt obligation ratio, kt − yt −β23i3m, in line with Minsky (1993),Kiyotaki and Moore (1997) etc. From the α-coefficients, both real house prices, creditand GDP are found significant error correcting to the relation. Hence, increasing realcredit will drive real house price and vise versa. Taken from these results, it seemsthe relations is to be controlled by the monetary policymakers through the short terminterest rate.

The third relation, β3, is not quite in line with what was a priori expected. Yet, therelation seems essential in the system and, taken from the α-coefficient, important forboth the house price dynamics and the risk appetite of the financial markets. From se-veral asset price theories, for instance C-CAPM models, the risk adjusted rate of returnon a given asset varies with agents’ risk aversion, which is a possible interpretation.Hereby, the risk appetite of financial markets also has an important role for house priceinflation. Importantly, the broken trend does not cancel in the relation, neither in thenon-identifying tests in appendix C. It means that other factor exogenous to the relationis essential to explain the constant growing house prices from 1993. The last relationlooks like a standard AD relation, thought, only with the short term interest rate errorcorrecting.

Figure 4: Cointegration relations of the modelβ1xt β2xt β3xt β4xt

1990 2000 2010

−1.00

−0.75

−0.50

1990 2000 2010

5.6

5.8

1990 2000 2010

5.25

5.50

1990 2000 2010

7.3

7.4

7.5

7.6

1990 2000 2010

−1.00

−0.75

−0.50

1990 2000 2010

5.6

5.8

1990 2000 2010

5.25

5.50

1990 2000 2010

7.3

7.4

7.5

7.6

1990 2000 2010

−1.00

−0.75

−0.50

1990 2000 2010

5.6

5.8

1990 2000 2010

5.25

5.50

1990 2000 2010

7.3

7.4

7.5

7.6

1990 2000 2010

−1.00

−0.75

−0.50

1990 2000 2010

5.6

5.8

1990 2000 2010

5.25

5.50

1990 2000 2010

7.3

7.4

7.5

7.6

The cointegration relations of the model is shown in figure 4. Seen from β1xt andβ2xt the period before the global financial crisis was associated with both excess liq-uidity and house prices rising faster - or the interest rate being lower - than what theleverage of of the economy dictated. In that perspective, the crisis period came as anequilibrium adjustment. Similar, though less extreme, is seen before both the S&L andto some extend the dot-com crisis periods. The most interesting question is why houseprices have been building up before the crisis periods when it should be equilibriumreacting to β2xt and unrelated to β1xt . Figure D.1 in appendix C shows the recursivegraphs for the α-coefficients as a check for possible inconstancy of the α-matrix - asalso was indicated in section 5.3. From around 2003 the α-coefficients of the real houseprice reaction to β1xt and β2xt does seem to jump. This might indicate that the equilib-

24

rium adjustments related to β2xt has been weekend, while a possible positive reaction toexcess liquidity has been strengthened. At the same time a clear jump is also seen in thereaction of real money to β2xt , indicating a possible connection. This is in line with theresults of Goodhart and Hofmann (2008) and to some extend Kaufmann and Valderrama(2007) and others that the multidirectional effects between house prices, monetary vari-ables and real credit becomes stronger in certain periods, especially periods of boominghouse prices.

6.2 Generalized impulse response functionsGiven the identified structure in table 7, I further analyzed GIR functions given by (12)in section 2.2. I generally consider 40 month responses from positive one standarddeviation shocks in εi. To illustrate significance, gray bands show 95% confidencebounds, calculated as boot strap simulations with 500 replications.34 The full rage ofGIR functions are shown in appendix C. Figure 5 below shows a relevant subset ofGIR function showing the effect of shocks to the real credit residuals. As expected realhouse prices do respond positively, significant even after 40 months. The real quantityof money also respond positively, though only significant so for 12 months. Real GDPand the credit spread also response significantly in the long run.

Figure 5: GIR - responses to positive shocks to εk(A) Response in h to εk (B) Response in m to εk (C) Response in y to εk (D) Response in cs to εk

Responses in HOUSEP_FP_LOG from a 1 std shock to epsilon(KREDIT_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−1.5

−1

−0.5

0

0.5

1

1.5

2

2.5

3

x 10−3

Response Confidence

Responses in M2M_FP_LOG from a 1 std shock to epsilon(KREDIT_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.5

0

0.5

1

1.5

2

2.5

3

3.5

x 10−3

Response Confidence

Responses in GDP_FP_LOG from a 1 std shock to epsilon(KREDIT_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

0

5

10

15

20

x 10−4

Response Confidence

Responses in SPREAD_CB_GB from a 1 std shock to epsilon(KREDIT_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−4

−3

−2

−1

0

1

2

3

4

x 10−4

Response Confidence

In relation to shocks to real money residuals, as shown in figure 6, real house pricesonly respond in the short run, i.e. positive and significant for three months. This couldbe due to the interpretation fallacy of GIR functions, meaning the endogeneity relatedproblem of separating monetary policy shocks from the economic situation they reactaccording to. For example, during the global financial crisis the federal reserve loweredthe federal funds rate and managed to increase the money supply by heavy quantitativeeasing, among others, in response to rapid falling house prices. However, real credit and

34These are calculated by...

25

real GDP does respond positively, also in the long run.

Figure 6: GIR - responses to positive shocks to εm(A) Response in h to εm (B) Response in k to εm (C) Response in y to εm (D) Response in cs to εm

Responses in HOUSEP_FP_LOG from a 1 std shock to epsilon(M2M_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−2.5

−2

−1.5

−1

−0.5

0

0.5

1

1.5

2

x 10−3

Response Confidence

Responses in KREDIT_FP_LOG from a 1 std shock to epsilon(M2M_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−1

−0.5

0

0.5

1

1.5

2

x 10−3

Response Confidence

Responses in GDP_FP_LOG from a 1 std shock to epsilon(M2M_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.5

0

0.5

1

1.5

2

2.5

3

x 10−3

Response Confidence

Responses in SPREAD_CB_GB from a 1 std shock to epsilon(M2M_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−4

−3

−2

−1

0

1

2

3

4

x 10−4

Response Confidence

Relevant responses to shocks to real house prices residuals are shown in figure 7.Real credit respond significant to these shocks only for 20 months, hereafter borderlineso. Hence, in regard to credit asset prices cycles, it seems real credit shocks is thedominating driving force rather then the opposite. For the rest of the variables, thereaction is only temporary and for credit spreads only after 20 months.

Figure 7: GIR - responses to shocks to εh(A) Response in k to εh (B) Response in m to εh (C) Response in y to εh (D) Response in cs to εh

Responses in KREDIT_FP_LOG from a 1 std shock to epsilon(HOUSEP_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.5

0

0.5

1

1.5

2

2.5

3

x 10−3

Response Confidence

Responses in M2M_FP_LOG from a 1 std shock to epsilon(HOUSEP_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−4

−3

−2

−1

0

1

2

3

4

x 10−3

Response Confidence

Responses in GDP_FP_LOG from a 1 std shock to epsilon(HOUSEP_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−1.5

−1

−0.5

0

0.5

1

1.5

2

2.5

x 10−3

Response Confidence

Responses in SPREAD_CB_GB from a 1 std shock to epsilon(HOUSEP_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−5

−4

−3

−2

−1

0

1

2

3

4

5

x 10−4

Response Confidence

6.3 The structural VMA representationIn order to analyze and possibly resolve the interpretation fallacy of the GIR functions,this section considerers SIR. Firstly, I have to make assumption about the restrictionsformulations, i.e. the causality ordering for permanent and temporary shocks respec-tively (see section 2.2). Here, I will assume that the permanent structural shocks areordered as follows: i) real economic supply shocks, up1[ys], ii) expansionary monetarypolicy shocks, up2[mp], and iii) real credit shocks, up3[k]. I hereby assume that real GDPin the long run only react to the first structural shock, i.e. supply shocks. Likewise, itis assumes that monetary policy - here assumed to be the control of the short interestrate as is the case for the Fed - in the long run only respond to the first and the se-cund structural shock. The last permanent structural shock is assumed to be real creditshocks. This restriction formulation, or the like, is a controversial assumption but quitecommon to the literature. Possibly it will eliminated some of the endogeneity problemsdescribed above. Less important, the temporary structural shocks are likewise ordered:i) real money shocks, ii) shocks to the risk appetite in financial markets, iii) shocks tothe own rate on money and iv) house prices shocks. Similar to (10) i section 2.1, (18)shows the identified C-matrix related to the permanent shocks, while the full identified

26

system, including the temporary restriction of C∗(L)B−1-matrix, is shown in (D.1) inappendix C.

htktmtyt

iown,ti3m,tcst

=

0.00−1

0.00(−2.52)

0.00(−8.97)

0∗

0∗

0∗

0∗

0.00(−2.39)

0.00(−2.28)

0.00(−8.97)

0∗

0∗

0∗

0∗

0.00(−5.29)

0.00(−4.08)

0.00(−8.97)

0∗

0∗

0∗

0∗

0.00(−5.83)

0

0

0 0 0 0

0.03−1.12

0.08(−4.15)

−0.01(−8.97)

0 0 0 0

0.02−0.97

0.07(−4.35)

0

0 0 0 0

0.00−1

0.00(−2.52)

0.00(−8.97)

..0.. ..0.. ..0.. ..0..

︸ ︷︷ ︸

C

∑ti=1 up1[ys],i

∑ti=1 up2[mp],i

∑ti=1 up3[k],i

∑ti=1 ut4[m],i

∑ti=1 ut5[cs],i

∑ti=1 ut6[iown],i

∑ti=1 ut7[h],i

+ · · · (18)

From this identified system all SIR functions are shown in appendix C. Figure 8 belowshows relevant responses (resp.) to real structural credit shocks. Apart from the long runrestrictions on the responses in real money and real GDP, the responses are quite similarto the GIR responses to shocks to the real credit residuals. Figure 9 shows the responsesto expansionary monetary policy shocks. Here, house prices do respond positively inthe long run. Hence, the suspicion regarding endogeneity in the monetary responsesseems justified, and it is possible to influence on house prices by means of monetarypolicy. Real credit now reacts insignificantly.

Figure 8: SIR - responses to positive real credit shocks(A) Resp. in h to up3[k] (B) Resp. in m to up3[k] (C) Resp. in y to up3[k] (D) Resp. in cs to up3[k]

Responses in HOUSEP_FP_LOG from a unit shock to s_3 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−3

−2

−1

0

1

2

3

4

5

6

x 10−3

Response Confidence

Responses in M2M_FP_LOG from a unit shock to s_3 with95 % confidence region

Month0 5 10 15 20 25 30 35 40

−5

−4

−3

−2

−1

0

1

x 10−3

Response Confidence

Responses in GDP_FP_LOG from a unit shock to s_3 with95 % confidence region

Month0 5 10 15 20 25 30 35 40

−1

−0.5

0

0.5

1

1.5

x 10−3

Response Confidence

Responses in SPREAD_CB_GB from a unit shock to s_3 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−1.5

−1

−0.5

0

0.5

1x 10

−3

Response Confidence

Figure 9: SIR - responses to expansionary monetary policy shocks(A) Resp. in h to up2[mp] (B) Resp. in k to up2[mp] (C) Resp. in y to up2[mp] (D) Resp. in cs to up2[mp]

Responses in HOUSEP_FP_LOG from a unit shock to s_2 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−1

0

1

2

3

4

5

6

x 10−3

Response Confidence

Responses in KREDIT_FP_LOG from a unit shock to s_2 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−4

−3

−2

−1

0

1

2

x 10−3

Response Confidence

Responses in GDP_FP_LOG from a unit shock to s_2 with95 % confidence region

Month0 5 10 15 20 25 30 35 40

−3

−2.5

−2

−1.5

−1

−0.5

0

0.5

1

1.5

x 10−3

Response Confidence

Responses in SPREAD_CB_GB from a unit shock to s_2 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−5

0

5

10

15

x 10−4

Response Confidence

27

6.4 Effects of exogenous policy shocks and regimesIn this section I do another type of IR analysis focusing more directly on plausibleexogenous shocks to the model. Using the methods of Blanchard and Perotti (2002), Ianalyze IR of shocks from the unrestricted shifts and the included impulse dummy in1984:11, Dp84:11. The shifts in 1990:01 and 2008:01 both represent the start of financialcrisis periods. The shift in 1993:07 represent the start of a prolonged period of constantcredit growth, possibly as a result of financial deregulation and innovation. In the end,the impulse dummy in 1984:11 represent a large expansionary monetary policy shockwhere the federal fund rate was lowered from 11.23% to 8.92%.

Figure 10: IR to exogenous deterministic components(A) Resp. in h to s90:01 (B) Resp. in k to s90:01 (C) Resp. in m to s90:01 (D) Resp. in y to s90:01

Responses in HOUSEP_FP_LOG from one unit shock to S1990_01 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−6

−4

−2

0

2

4x 10

−3

Response Confidence

Responses in KREDIT_FP_LOG from one unit shock to S1990_01 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−8

−6

−4

−2

0

2

4

x 10−3

Response Confidence

Responses in M2M_FP_LOG from one unit shock to S1990_01 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−4

−3

−2

−1

0

1

2

3

4

5

6

x 10−3

Response Confidence

Responses in GDP_FP_LOG from one unit shock to S1990_01 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−4.5

−4

−3.5

−3

−2.5

−2

−1.5

−1

−0.5

0

0.5

x 10−3

Response Confidence

(F) Resp. in h to s93:07 (G) Resp. in k to s93:07 (H) Resp. in m to s93:07 (I) Resp. in y to s93:07 Responses in HOUSEP_FP_LOG from one unit shock to S1993_07 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−4

−3

−2

−1

0

1

2

3

4

x 10−3

Response Confidence

Responses in KREDIT_FP_LOG from one unit shock to S1993_07 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−2

−1

0

1

2

3

4

5

6

7

8

x 10−3

Response Confidence

Responses in M2M_FP_LOG from one unit shock to S1993_07 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−7

−6

−5

−4

−3

−2

−1

0

1

2

3

x 10−3

Response Confidence

Responses in GDP_FP_LOG from one unit shock to S1993_07 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−3

−2

−1

0

1

2

3

x 10−3

Response Confidence

(J) Resp. in h to s08:01 (K) Resp. in k to s08:01 (L) Resp. in m to s08:01 (M) Resp. in y to s08:01 Responses in HOUSEP_FP_LOG from one unit shock to S2008_01 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.03

−0.025

−0.02

−0.015

−0.01

−0.005

0

0.005

0.01

0.015 Response Confidence

Responses in KREDIT_FP_LOG from one unit shock to S2008_01 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−14

−12

−10

−8

−6

−4

−2

0

2

x 10−3

Response Confidence

Responses in M2M_FP_LOG from one unit shock to S2008_01 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.025

−0.02

−0.015

−0.01

−0.005

0

0.005

0.01 Response Confidence

Responses in GDP_FP_LOG from one unit shock to S2008_01 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−10

−8

−6

−4

−2

0

2

4

x 10−3

Response Confidence

(N) Resp. in h to Dp84:11 (P) Resp. in k to Dp84:11 (Q) Resp. in m to Dp84:11 (R) Resp. in y to Dp84:11 Responses in HOUSEP_FP_LOG from one unit shock to DP1984_11 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.01

−0.005

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035 Response Confidence

Responses in KREDIT_FP_LOG from one unit shock to DP1984_11 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.005

0

0.005

0.01

0.015

0.02

0.025 Response Confidence

Responses in M2M_FP_LOG from one unit shock to DP1984_11 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.01

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07 Response Confidence

Responses in GDP_FP_LOG from one unit shock to DP1984_11 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.005

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04 Response Confidence

As seen the shift dummies only affect real house prices and real credit, which isagreement with the findings that these shifts resolve a problem of I(2) trends or bro-ken deterministic trends only in these variables. While the crises periods influence asexpected, the shift in 1993:07 primarily affect real credit, this might suggest that thisshift initially is affecting real credit while real house prices later react on the basis of theresulting credit expansion. Once again, this indicates that real credit is the dominatingdriving force of these cycles, rather then the real house prices. However, one has to keep

28

in mind that these shift are found on the basis of real credit growth and not real houseprices, which might give bias results. In the end, all variable seems to react positivelyto the monetary policy shock of 1984:11, though somewhat slow for real house price.General, it seems clear that monetary policy shock has a much smaller effect on houseprices than the plausible shock of financial deregulation and innovation starting a creditboom from the middle of the 1990s.

7 An alternative specificationAs a robustness check I will do an alternative specification of the model using thenominal-to-real transformation suggested by Kongsted (2005). Here, I transform themodel to instead of including real house prices and real credit it includes real houseprice inflation, πh = ∆h, and the amount of credit evaluated in house prices, k−h. Froma univariate Dickey-Fuller test on ∆(k− h) and ∆πh they are both accepted stationary,though for ∆(k−h) only borderline so.35 In the miss specification test I also find that themodel should include two lags, a restricted shift in 2008:08, while not all the same im-pulse dummies as in the previous model: 1984:07, 1984:11, 1987:10, 1992:07, 1998:04,2001:09, 2005:09, 2006:09, 2008:02, 2008:05, 2008:10, 2008:11, 2009:01. Hereby themodel seems fairly well specified. More critical, table 8 below shows the I(1) trace testand the unrestricted roots of the model. Here the trace test clearly indicated a rank ofr = 4, while the unrestricted root calculations indicate a rank of r = 3. This indicatesthat the transformation does not model the trend breaks perfectly. However, comparingwith the unrestricted roots of the untransformed model shown in table C.1 in appendixB the transformed models seems much closer to the I(1) CVAR scenario. To be ableto compare with the model specified earlier, I set the rank r = 4 well knowing that themodel includes a fairly large unrestricted root of 0.951.

Table 8: I(1) trace test and the unrestricted rootsr 0 1 2 3 4 5 6 7p-r 7 6 5 4 3 2 1 0p∗Bart 0.000 0.000 0.000 0.001 0.061 0.342 0.998 -ρmax 0.658 0.714 0.883 0.906 0.956 0.984 0.990 0.990

Note: p∗Bart is the Bartlett corrected trace test and ρmax is the largest unrestricted root of the Π-matrix.

35The test results are; DF(∆(k− h)) = −2.84∗ and DF(∆πh) = −7.60∗∗, with the critical values:1%≈−3.45∗∗, 5%≈−2.87∗, 10%≈−2.57. The regressions include 11 lags.

29

Table 9: The transformed credit CVAR model on ECM-formπh k−h m y iown i3M cs s08:08 t

β1 - - 1.00−

−1.00−

−0.12(−21.47)

0.12(21.47)

−4.82(−7.23)

- -

β2 - 1.00−

- −1.00−

- −0.08(−13.31)

- −0.15(−5.71)

0.00(−12.36)

β3 1.00−

- - - - - −16.51(−5.22)

0.70(7.78)

-

β4 - - - 1.00−

−0.05(−13.87)

- - 0.11(8.33)

0.00(−54.22)

∆πh ∆(k−h) ∆m ∆y ∆iown ∆i3M ∆csα1 1.38

(3.74)0.01(1.72)

−0.03(−5.49)

0.03(6.32)

0.23(4.63)

−0.01(−0.03)

0.00(1.13)

α2 0.95(3.15)

−0.01(−3.92)

−0.01(−2.23)

0.03(6.74)

−0.06(−1.42)

−0.20(−1)

0.01(2.34)

α3 −0.21(−3.69)

−0.01(−9.37)

0.00(4.38)

0.00(2.9)

−0.01(−0.71)

−0.02(−0.6)

0.00(3.14)

α4 0.43(0.5)

0.03(2.99)

−0.03(−1.87)

−0.03(−2.77)

0.66(5.63)

1.96(3.55)

−0.03(−3.5)

Note: i) P-values are shown in the parenthesis. ii) Significant coefficients are marked bold. iii)Significant error correcting coefficients are marked red ind the α-matrix.

The model shown in table 9 is identified with a p-value of 0.097. The restrictionformulation is quite similar to the earlier model and it also does give somewhat thesame conclusions. On the central points, excess money supply has a positive effect onhouse price inflation giving some support to the money view. Excess real credit in therelations of credit asset price cycles also has a positive effect on house price inflationalso supporting the credit view. In the end, risk appetite in the financial markets alsoaffects.

8 Concluding remarksIn the presents paper, I have analyzed the macroeconomic mechanisms of financial in-stability between real house prices, credit and money within a I(1) CVAR model. It hasbeen argued that ... [MORE HERE]

30

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34

Appendix B: Data sources and data constructionThe data of the model consist of the following seven variables, where for the nominal-to-real calculations I use the CPI, all items city average:

Table B.1: The variables of the model

Ticker Description Sourceh Real house prices (log) House Prices, Freddie Mac, national, index (quar-

terly).k Real credit (log) Debt Outstanding, domestic nonfinancial sectors,

total, SA, USD (quarterly).m The real quantity of money, M2M (log) Money supply M2, SA, USD, excluding small

time deposits.y Real GDP (log) GDP, overall, total, CP, AR, SA, USD (quarterly).iown The nominal own rate on money M2M Money Stock, own ratei3m Three month t-bill rate Treasury bills, bid, 3 month, yield, averagecs Corporate bond credit spread The difference between: i) corporate benchmarks,

Moody’s Baa-rated long-term, yield, average andii) government benchmarks, bid, 10 year, yield,average

Three series are quarterly; h, k and y. Before the nominal-to-real transformation Ido a Chow-Lin interpolation from quarterly to monthly frequencies with the followingmonthly series:

Table B.2: The variables of the modelTicker Quarterly series Monthly series (source)h Real house prices (log) House Prices, S&P Case-Shiller, composite-10,

index (from 1987).k Real credit (log) Consumer credit, total, SA, USD.y Real GDP (log) i) US national unemployment rate, ii) BIS, real

narrow effective exchange rate, index, average, iii)US industrial production.

35

Appendix C: Model specification

Cointegration rank of the model without deterministic terms

Figure C.1: The R-form and normal cointegration relations of the model without deter-ministic terms included

β1xt β2xtBeta1’*Z1(t)

1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-8

-6

-4

-2

0

2

4

Beta1’*R1(t)

1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-6

-4

-2

0

2

4

Beta2’*Z1(t)

1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-4

-3

-2

-1

0

1

2

3

4

5

Beta2’*R1(t)

1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-4

-3

-2

-1

0

1

2

3

4β1Rt β2Rt

Beta1’*Z1(t)

1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-8

-6

-4

-2

0

2

4

Beta1’*R1(t)

1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-6

-4

-2

0

2

4

Beta2’*Z1(t)

1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-4

-3

-2

-1

0

1

2

3

4

5

Beta2’*R1(t)

1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-4

-3

-2

-1

0

1

2

3

4

β3xt β4xtBeta3’*Z1(t)

1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-4

-3

-2

-1

0

1

2

3

4

Beta3’*R1(t)

1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-2

-1

0

1

2

3

Beta4’*Z1(t)

1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-3

-2

-1

0

1

2

3

Beta4’*R1(t)

1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-3

-2

-1

0

1

2

3β3Rt β4Rt

Beta3’*Z1(t)

1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-4

-3

-2

-1

0

1

2

3

4

Beta3’*R1(t)

1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-2

-1

0

1

2

3

Beta4’*Z1(t)

1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-3

-2

-1

0

1

2

3

Beta4’*R1(t)

1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-3

-2

-1

0

1

2

3

36

Table C.1: The largest unrestricted root and the trace test of the model only includingimpulse dummies

p− r r 7 6 5 4 3 2 1 07 0 −

(0.00)−

(0.00)−

(0.00)−

(0.00)−

(0.00)−

(0.00)−

(0.00)−

(0.00)

6 1 −0.119(0.00)

−0.063(0.00)

0.187(0.00)

0.386(0.00)

0.517(0.00)

0.773(0.00)

0.953(0.00)

5 2 0.186(0.00)

0.398(0.00)

0.455(0.00)

0.775(0.00)

0.836(0.00)

0.921(0.00)

4 3 0.449(0.00)

0.767(0.00)

0.846(0.00)

0.88(0.00)

0.961(0.00)

3 4 0.841(0.01)

0.893(0.06)

0.941(0.03)

0.975(0.06)

2 5 0.93(0.24)

0.971(0.27)

0.978(0.37)

1 6 0.99(0.89)

0.991(0.65)

0 7 0.99(−)

Note: In the table numbers, ρmax(p−value) , ρmax indicate the largest unrestricted root of the characteristic

polynomial of the Π-matrix, while the lower indicate the p-values of the trace test.

A univariate two stage Markov switching model in credit in first dif-ference

Table C.2: Regime classification based on smoothed probabilitiesRegime 0 Months Average probability1990:01 - 1993:06 42 0.9642008:01 - 2008:07 7 0.9062009.01 - 2010:06 18 0.989Regime 11984:04 - 1989:12 88 0.9921993:07 - 2007:12 174 0.9842008:08 - 2008:12 5 0.862

Note: Regime 0 totally has 67 months (20.06%) with an average duration of 22.33 months. Regime 1totally has 267 months (79.94%) with an average duration of 89.00 months.

37

Figure C.2: Actual and fitted values

Cointegration rank of the model with deterministic terms

Table C.3: The largest unrestricted root and the trace test of the model including impulsedummies, a restricted shift in 2008:08 and three trend breaks

ρmax(p−value)

s2 = p− r− s1

p− r = s1 + s2 r 7 6 5 4 3 2 1 07 0 −

(0.00)−

(0.00)−

(0.00)−

(0.00)−

(0.00)−

(0.00)−

(0.00)−

(0.00)

6 1 −0.213(0.00)

−0.114(0.00)

−0.157(0.00)

0.37(0.00)

0.502(0.00)

0.766(0.00)

0.808(0.00)

5 2 −0.115(0.00)

0.388(0.00)

0.467(0.00)

0.773(0.00)

0.816(0.00)

0.857(0.00)

4 3 0.438(0.00)

0.757(0.00)

0.828(0.00)

0.925(0.00)

0.930(0.00)

3 4 0.829(0.00)

0.935(0.00)

0.929(0.00)

0.918(0.00)

2 5 0.927(0.01)

0.933(0.03)

0.930(0.08)

1 6 0.947(0.09)

0.974(0.10)

0 7 0.98(−)

Note: In the table numbers, ρmax(p−value) , ρmax indicate the largest unrestricted root of the characteristic

polynomial of the Π-matrix, while the lower indicate the p-values of the trace test.

38

Constancy of the parameters

Figure C.3: Test for constancy of the log-likelihoodTest with baseline-period 1983:06 - 1989:01 Test with baseline-period 2005:02 - 2010:09Test for Constancy of the Log-Likelihood

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 20090

1

2

3

4

5X(t)R1(t)5% C.V. (1.36 = Index)

Test for Constancy of the Log-Likelihood

1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 20030.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5X(t)R1(t)5% C.V. (1.36 = Index)

Note: i) The lowest graph indicates the log-likelihood on R-form, meaning that the short run structure isregressed out (see Juselius (2007, s.151)). ii) The horizontal line determines the 5 % confidence level fora constant log-likelihood (parameter regime).

Figure C.4: Test for constancy of the beta-matrixTest with baseline-period 1983:06 - 1989:01 Test with baseline-period 2005:02 - 2010:09Test of Beta Constancy

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 20090.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75X(t)R1(t)5% C.V. (6.45 = Index)

Q(t)

Test of Beta Constancy

1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 20030.0

0.2

0.4

0.6

0.8

1.0

1.2X(t)R1(t)5% C.V. (6.45 = Index)

Q(t)

Note: i) The lowest graph indicates the log-likelihood on R-form, meaning that the short run structure isregressed out (see Juselius (2007, s.151)). ii) The horizontal line determines the 5 % confidence level fora constant log-likelihood (parameter regime).

39

Appendix D: Empirical results

The VECM identification

Table D.1: Theoretical consistent non-identifying tested on the cointegration structurep-value h k m y iown i3M cs s08:08 bt93:07 t α∆h

(A) Money demand relation (13)H1 0.166 - - 1.00

−−1.35(−17.92)

−0.08(−8.4)

0.08(8.40)

−4.85(−3.29)

−0.35(−5.85)

- - 0.01(3.56)

H2 0.033 - - 1.00−

−1.00−

−0.14(−9.71)

0.14(9.71)

−2.68(−1.18)

−0.42(−4.58)

- - 0.02(2.96)

H3 0.038 - - 1.00−

−1.00−

−0.15(−11.67)

0.15(11.67)

- −0.48(−5.12)

- - 0.01(0.70)

H4 0.020 −0.06(−0.83)

- 1.00−

−1.00−

−0.14(−11.54)

0.14(11.54)

- −0.43(−5.33)

- - 0.01(0.83)

(B) Leverage asset price cycle relation (14)H5 0.265 1.00

−−1.21(−16.69)

- 1.21(16.69)

- 0.02(4.68)

- −0.22(−3.79)

- - −0.02(−3.80)

H6 0.138 1.00−

−1.00−

- 1.00−

- 0.04(6.67)

- −0.26(−3.44)

- - −0.02(−4.08)

H7 0.259 1.00−

−1.00−

- 1.00−

- 0.02(4.40)

- −0.21(−3.85)

- 0.00(−3.14)

−0.02(−3.38)

H8 0.150 1.00−

−1.00−

- 1.00−

- 0.02(4.04)

- −0.20(−3.92)

−0.00(−0.607)

−0.00(−0.616)

−0.02(−3.17)

(C) Basic cycle relation (15)H9 0.019 - - - 1.00

−- - 4.10

(3.49)0.37(6.69)

- 0.00(−13.4)

−0.02(−3.64)

H10 0.005 1.00−

- - - - - −5.08(−1.95)

−0.69(−5.73)

- 0.00(−10.08)

0.01(3.10)

H11 0.704 1.00−

- - - - - 10.18(5.95)

0.15(1.89)

−0.01(−5.93)

0.00(1.19)

−0.01(−3.45)

40

Parameter constancy

Figure D.1: Recursive graphs for the α-coefficientsα1,h α2,h α3,h α4,hAlpha 1 (X-model)

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.075

-0.050

-0.025

0.000

0.025

0.050

0.075DHOUSEP_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.25

-0.20

-0.15

-0.10

-0.05

-0.00

0.05DKREDIT_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.25

-0.20

-0.15

-0.10

-0.05

-0.00

0.05DM2M_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20DGDP_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0DOWN_M2M

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-20.0

-17.5

-15.0

-12.5

-10.0

-7.5

-5.0

-2.5

0.0

2.5DTBILL_3M_CURP

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

-0.00

0.05DSPREAD_CB_GB

Alpha 2 (X-model)

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.10

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06DHOUSEP_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25DKREDIT_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15DM2M_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15DGDP_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0DOWN_M2M

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-15

-10

-5

0

5

10

15DTBILL_3M_CURP

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.25

-0.20

-0.15

-0.10

-0.05

-0.00

0.05

0.10DSPREAD_CB_GB

Alpha 3 (X-model)

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.15

-0.10

-0.05

0.00

0.05

0.10DHOUSEP_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5DKREDIT_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.2

-0.1

0.0

0.1

0.2

0.3

0.4DM2M_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3DGDP_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-2

-1

0

1

2

3

4

5DOWN_M2M

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-10

0

10

20

30

40DTBILL_3M_CURP

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6DSPREAD_CB_GB

Alpha 4 (X-model)

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.075

-0.050

-0.025

0.000

0.025

0.050

0.075

0.100DHOUSEP_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.05

0.00

0.05

0.10

0.15

0.20

0.25DKREDIT_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15DM2M_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

-0.00

0.05

0.10DGDP_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-1.0

-0.5

0.0

0.5

1.0

1.5

2.0DOWN_M2M

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-7.5

-5.0

-2.5

0.0

2.5

5.0

7.5

10.0

12.5DTBILL_3M_CURP

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30DSPREAD_CB_GB

α1,k α2,k α3,k α4,k

Alpha 1 (X-model)

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.075

-0.050

-0.025

0.000

0.025

0.050

0.075DHOUSEP_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.25

-0.20

-0.15

-0.10

-0.05

-0.00

0.05DKREDIT_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.25

-0.20

-0.15

-0.10

-0.05

-0.00

0.05DM2M_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20DGDP_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0DOWN_M2M

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-20.0

-17.5

-15.0

-12.5

-10.0

-7.5

-5.0

-2.5

0.0

2.5DTBILL_3M_CURP

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

-0.00

0.05DSPREAD_CB_GB

Alpha 2 (X-model)

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.10

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06DHOUSEP_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25DKREDIT_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15DM2M_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15DGDP_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0DOWN_M2M

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-15

-10

-5

0

5

10

15DTBILL_3M_CURP

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.25

-0.20

-0.15

-0.10

-0.05

-0.00

0.05

0.10DSPREAD_CB_GB

Alpha 3 (X-model)

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.15

-0.10

-0.05

0.00

0.05

0.10DHOUSEP_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5DKREDIT_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.2

-0.1

0.0

0.1

0.2

0.3

0.4DM2M_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3DGDP_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-2

-1

0

1

2

3

4

5DOWN_M2M

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-10

0

10

20

30

40DTBILL_3M_CURP

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6DSPREAD_CB_GB

Alpha 4 (X-model)

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.075

-0.050

-0.025

0.000

0.025

0.050

0.075

0.100DHOUSEP_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.05

0.00

0.05

0.10

0.15

0.20

0.25DKREDIT_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15DM2M_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

-0.00

0.05

0.10DGDP_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-1.0

-0.5

0.0

0.5

1.0

1.5

2.0DOWN_M2M

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-7.5

-5.0

-2.5

0.0

2.5

5.0

7.5

10.0

12.5DTBILL_3M_CURP

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30DSPREAD_CB_GB

α1,m α2,m α3,m α4,m

Alpha 1 (X-model)

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.075

-0.050

-0.025

0.000

0.025

0.050

0.075DHOUSEP_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.25

-0.20

-0.15

-0.10

-0.05

-0.00

0.05DKREDIT_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.25

-0.20

-0.15

-0.10

-0.05

-0.00

0.05DM2M_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20DGDP_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0DOWN_M2M

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-20.0

-17.5

-15.0

-12.5

-10.0

-7.5

-5.0

-2.5

0.0

2.5DTBILL_3M_CURP

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

-0.00

0.05DSPREAD_CB_GB

Alpha 2 (X-model)

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.10

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06DHOUSEP_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25DKREDIT_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15DM2M_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15DGDP_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0DOWN_M2M

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-15

-10

-5

0

5

10

15DTBILL_3M_CURP

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.25

-0.20

-0.15

-0.10

-0.05

-0.00

0.05

0.10DSPREAD_CB_GB

Alpha 3 (X-model)

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.15

-0.10

-0.05

0.00

0.05

0.10DHOUSEP_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5DKREDIT_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.2

-0.1

0.0

0.1

0.2

0.3

0.4DM2M_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3DGDP_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-2

-1

0

1

2

3

4

5DOWN_M2M

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-10

0

10

20

30

40DTBILL_3M_CURP

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6DSPREAD_CB_GB

Alpha 4 (X-model)

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.075

-0.050

-0.025

0.000

0.025

0.050

0.075

0.100DHOUSEP_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.05

0.00

0.05

0.10

0.15

0.20

0.25DKREDIT_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15DM2M_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

-0.00

0.05

0.10DGDP_FP_LOG

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-1.0

-0.5

0.0

0.5

1.0

1.5

2.0DOWN_M2M

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-7.5

-5.0

-2.5

0.0

2.5

5.0

7.5

10.0

12.5DTBILL_3M_CURP

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30DSPREAD_CB_GB

41

GIR functions

Figure D.2: Generalized impulse response functionsResponse in real house prices

Response in h to εh Response in h to εk Response in h to εm Response in h to εy Responses in HOUSEP_FP_LOG from a 1 std shock to epsilon(HOUSEP_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−1

0

1

2

3

4

5

6

7

x 10−3

Response Confidence

Responses in HOUSEP_FP_LOG from a 1 std shock to epsilon(KREDIT_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−1.5

−1

−0.5

0

0.5

1

1.5

2

2.5

3

x 10−3

Response Confidence

Responses in HOUSEP_FP_LOG from a 1 std shock to epsilon(M2M_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−2.5

−2

−1.5

−1

−0.5

0

0.5

1

1.5

2

x 10−3

Response Confidence

Responses in HOUSEP_FP_LOG from a 1 std shock to epsilon(GDP_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−3

−2.5

−2

−1.5

−1

−0.5

0

0.5

1

1.5

x 10−3

Response Confidence

Response in h to εiown Response in h to εi3m Response in h to εcs Responses in HOUSEP_FP_LOG from a 1 std shock to epsilon(OWN_M2M) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−4

−3.5

−3

−2.5

−2

−1.5

−1

−0.5

0

0.5

x 10−3

Response Confidence

Responses in HOUSEP_FP_LOG from a 1 std shock to epsilon(TBILL_3M_CURP) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−6

−5

−4

−3

−2

−1

0

1

x 10−3

Response Confidence

Responses in HOUSEP_FP_LOG from a 1 std shock to epsilon(SPREAD_CB_GB) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−3

−2

−1

0

1

2

3

x 10−3

Response Confidence

Response in real creditResponse in k to εh Response in k to εk Response in k to εm Response in k to εy

Responses in KREDIT_FP_LOG from a 1 std shock to epsilon(HOUSEP_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.5

0

0.5

1

1.5

2

2.5

3

x 10−3

Response Confidence

Responses in KREDIT_FP_LOG from a 1 std shock to epsilon(KREDIT_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−2

−1

0

1

2

3

x 10−3

Response Confidence

Responses in KREDIT_FP_LOG from a 1 std shock to epsilon(M2M_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−1

−0.5

0

0.5

1

1.5

2

x 10−3

Response Confidence

Responses in KREDIT_FP_LOG from a 1 std shock to epsilon(GDP_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−2

−1

0

1

2

3

x 10−3

Response Confidence

Response in k to εiown Response in k to εi3m Response in k to εcs Responses in KREDIT_FP_LOG from a 1 std shock to epsilon(OWN_M2M) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−1.5

−1

−0.5

0

0.5

1

1.5

x 10−3

Response Confidence

Responses in KREDIT_FP_LOG from a 1 std shock to epsilon(TBILL_3M_CURP) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−2

−1

0

1

2

3

4

x 10−3

Response Confidence

Responses in KREDIT_FP_LOG from a 1 std shock to epsilon(SPREAD_CB_GB) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−5

−4

−3

−2

−1

0

1

x 10−3

Response Confidence

Response in the real quantity of moneyResponse in m to εh Response in m to εk Response in m to εm Response in m to εy

Responses in M2M_FP_LOG from a 1 std shock to epsilon(HOUSEP_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−4

−3

−2

−1

0

1

2

3

4

x 10−3

Response Confidence

Responses in M2M_FP_LOG from a 1 std shock to epsilon(KREDIT_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.5

0

0.5

1

1.5

2

2.5

3

3.5

x 10−3

Response Confidence

Responses in M2M_FP_LOG from a 1 std shock to epsilon(M2M_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−3

−2

−1

0

1

2

3

4

5

6

x 10−3

Response Confidence

Responses in M2M_FP_LOG from a 1 std shock to epsilon(GDP_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−1

0

1

2

3

4

5

x 10−3

Response Confidence

Response in m to εiown Response in m to εi3m Response in m to εcs Responses in M2M_FP_LOG from a 1 std shock to epsilon(OWN_M2M) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−6

−4

−2

0

2

4x 10

−3

Response Confidence

Responses in M2M_FP_LOG from a 1 std shock to epsilon(TBILL_3M_CURP) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−12

−10

−8

−6

−4

−2

0

2

x 10−3

Response Confidence

Responses in M2M_FP_LOG from a 1 std shock to epsilon(SPREAD_CB_GB) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−4

−3

−2

−1

0

1

2

3

4

5

6

x 10−3

Response Confidence

Note: i) Impulse-response for each individual variable to a positive one-std. shock to one of the errorterms of the reduced-form model (ε) ii) The gray bands are 95 % confidence bounds calculated bybootstrap-simulation with 500 replications.

42

Figure D.3: Generalized impulse response functionsResponse in real GDP

Response in y to εh Response in y to εk Response in y to εm Response in y to εy Responses in GDP_FP_LOG from a 1 std shock to epsilon(HOUSEP_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−1.5

−1

−0.5

0

0.5

1

1.5

2

2.5

x 10−3

Response Confidence

Responses in GDP_FP_LOG from a 1 std shock to epsilon(KREDIT_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

0

5

10

15

20

x 10−4

Response Confidence

Responses in GDP_FP_LOG from a 1 std shock to epsilon(M2M_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.5

0

0.5

1

1.5

2

2.5

3

x 10−3

Response Confidence

Responses in GDP_FP_LOG from a 1 std shock to epsilon(GDP_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−2

−1

0

1

2

3

4

5

x 10−3

Response Confidence

Response in y to εiown Response in y to εi3m Response in y to εcs Responses in GDP_FP_LOG from a 1 std shock to epsilon(OWN_M2M) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−3

−2.5

−2

−1.5

−1

−0.5

0

0.5

x 10−3

Response Confidence

Responses in GDP_FP_LOG from a 1 std shock to epsilon(TBILL_3M_CURP) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−4

−3

−2

−1

0

1

2

x 10−3

Response Confidence

Responses in GDP_FP_LOG from a 1 std shock to epsilon(SPREAD_CB_GB) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−3.5

−3

−2.5

−2

−1.5

−1

−0.5

0

0.5

x 10−3

Response Confidence

Response in the nominal own rate on moneyResponse in iown to εh Response in iown to εk Response in iown to εm Response in iown to εy

Responses in OWN_M2M from a 1 std shock to epsilon(HOUSEP_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.03

−0.02

−0.01

0

0.01

0.02

0.03

0.04 Response Confidence

Responses in OWN_M2M from a 1 std shock to epsilon(KREDIT_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.01

−0.005

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035 Response Confidence

Responses in OWN_M2M from a 1 std shock to epsilon(M2M_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.03

−0.02

−0.01

0

0.01

0.02

0.03 Response Confidence

Responses in OWN_M2M from a 1 std shock to epsilon(GDP_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.03

−0.02

−0.01

0

0.01

0.02

0.03

0.04

0.05 Response Confidence

Response in iown to εiown Response in iown to εi3m Response in iown to εcs Responses in OWN

M2M from a 1 std shock to epsilon(OWN

M2M) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.02

0

0.02

0.04

0.06

0.08

0.1

Response Confidence

Responses in OWN_M2M from a 1 std shock to epsilon(TBILL_3M_CURP) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.05

0

0.05

0.1

0.15

Response Confidence

Responses in OWN_M2M from a 1 std shock to epsilon(SPREAD_CB_GB) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.1

−0.08

−0.06

−0.04

−0.02

0

0.02 Response Confidence

Response in the nominal three month government T-bill rateResponse in i3m to εh Response in i3m to εk Response in i3m to εm Response in i3m to εy

Responses in TBILL_3M_CURP from a 1 std shock to epsilon(HOUSEP_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.04

−0.02

0

0.02

0.04

0.06

0.08

0.1 Response Confidence

Responses in TBILL_3M_CURP from a 1 std shock to epsilon(KREDIT_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.04

−0.03

−0.02

−0.01

0

0.01

0.02

0.03

0.04

0.05

0.06 Response Confidence

Responses in TBILL_3M_CURP from a 1 std shock to epsilon(M2M_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.08

−0.06

−0.04

−0.02

0

0.02

0.04

0.06 Response Confidence

Responses in TBILL_3M_CURP from a 1 std shock to epsilon(GDP_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.04

−0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14 Response Confidence

Response in i3m to εiown Response in i3m to εi3m Response in i3m to εcs Responses in TBILL_3M_CURP from a 1 std shock to epsilon(OWN_M2M) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

0

0.05

0.1

0.15

0.2

Response Confidence

Responses in TBILL_3M_CURP from a 1 std shock to epsilon(TBILL_3M_CURP) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4 Response Confidence

Responses in TBILL_3M_CURP from a 1 std shock to epsilon(SPREAD_CB_GB) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.25

−0.2

−0.15

−0.1

−0.05

0

0.05

0.1 Response Confidence

Note: i) Impulse-response for each individual variable to a positive one-std. shock to one of the errorterms of the reduced-form model (ε) ii) The gray bands are 95 % confidence bounds calculated bybootstrap-simulation with 500 replications.

43

Figure D.4: Generalized impulse response functionsResponse in the credit spread

Response in cs to εh Response in cs to εk Response in cs to εm Response in cs to εy Responses in SPREAD_CB_GB from a 1 std shock to epsilon(HOUSEP_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−5

−4

−3

−2

−1

0

1

2

3

4

5

x 10−4

Response Confidence

Responses in SPREAD_CB_GB from a 1 std shock to epsilon(KREDIT_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−4

−3

−2

−1

0

1

2

3

4

x 10−4

Response Confidence

Responses in SPREAD_CB_GB from a 1 std shock to epsilon(M2M_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−4

−3

−2

−1

0

1

2

3

4

x 10−4

Response Confidence

Responses in SPREAD_CB_GB from a 1 std shock to epsilon(GDP_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−10

−8

−6

−4

−2

0

2

4

x 10−4

Response Confidence

Response in cs to εiown Response in cs to εi3m Response in cs to εcs Responses in SPREAD_CB_GB from a 1 std shock to epsilon(OWN_M2M) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−3

−2

−1

0

1

2

3

4

x 10−4

Response Confidence

Responses in SPREAD_CB_GB from a 1 std shock to epsilon(TBILL_3M_CURP) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−12

−10

−8

−6

−4

−2

0

2

4

6

x 10−4

Response Confidence

Responses in SPREAD_CB_GB from a 1 std shock to epsilon(SPREAD_CB_GB) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.5

0

0.5

1

1.5

2

2.5

x 10−3

Response Confidence

Note: i) Impulse-response for each individual variable to a positive one-std. shock to one of the errorterms of the reduced-form model (ε) ii) The gray bands are 95 % confidence bounds calculated bybootstrap-simulation with 500 replications.

Generalized impulse response functions for the cointegration relations:

44

Figure D.5: Generalized impulse response functions for the cointegration relationsResponse in the money demand cointegration relation (β1)

Response in β1 to εh Response in β1 to εk Response in β1 to εm Response in β1 to εy Responses in coint(1) from a 1 std shock to epsilon(HOUSEP_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−2

0

2

4

6

8

10

x 10−3

Response Confidence

Responses in coint(1) from a 1 std shock to epsilon(KREDIT_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−4

−3

−2

−1

0

1

2

3

4

5

6

x 10−3

Response Confidence

Responses in coint(1) from a 1 std shock to epsilon(M2M_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−4

−2

0

2

4

6

8

x 10−3

Response Confidence

Responses in coint(1) from a 1 std shock to epsilon(GDP_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−4

−2

0

2

4

6

8

10

12

x 10−3

Response Confidence

Response in β1 to εiown Response in β1 to εi3m Response in β1 to εSCB−GB Responses in coint(1) from a 1 std shock to epsilon(OWN

M2M) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−4

−2

0

2

4

6

8

10

12

14

x 10−3

Response Confidence

Responses in coint(1) from a 1 std shock to epsilon(TBILL_3M_CURP) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.01

−0.005

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035 Response Confidence

Responses in coint(1) from a 1 std shock to epsilon(SPREAD_CB_GB) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.025

−0.02

−0.015

−0.01

−0.005

0

0.005 Response Confidence

Response in the Asset Boom-Bust cointegration relation (β2)Response in β2 to εh Response in β2 to εk Response in β2 to εm Response in β2 to εy

Responses in coint(2) from a 1 std shock to epsilon(HOUSEP_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−2

0

2

4

6

8

10

x 10−3

Response Confidence

Responses in coint(2) from a 1 std shock to epsilon(KREDIT_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−3

−2

−1

0

1

2

3

4

x 10−3

Response Confidence

Responses in coint(2) from a 1 std shock to epsilon(M2M_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−3

−2

−1

0

1

2

3

4

5

x 10−3

Response Confidence

Responses in coint(2) from a 1 std shock to epsilon(GDP_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−2

0

2

4

6

8

10

x 10−3

Response Confidence

Response in β2 to εiown Response in β2 to εi3m Response in β2 to εSCB−GB Responses in coint(2) from a 1 std shock to epsilon(OWN

M2M) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−4

−2

0

2

4

6

8

10

12

x 10−3

Response Confidence

Responses in coint(2) from a 1 std shock to epsilon(TBILL_3M_CURP) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.005

0

0.005

0.01

0.015

0.02

0.025 Response Confidence

Responses in coint(2) from a 1 std shock to epsilon(SPREAD_CB_GB) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−20

−15

−10

−5

0

5

x 10−3

Response Confidence

Response in the AD/credit relation cointegration relation (β3)Response in β3 to εh Response in β3 to εk Response in β3 to εm Response in β3 to εy

Responses in coint(3) from a 1 std shock to epsilon(HOUSEP_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−4

−2

0

2

4

6

8

10

12

x 10−3

Response Confidence

Responses in coint(3) from a 1 std shock to epsilon(KREDIT_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−3

−2

−1

0

1

2

3

4

5

6

x 10−3

Response Confidence

Responses in coint(3) from a 1 std shock to epsilon(M2M_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−4

−3

−2

−1

0

1

2

3

4

5

6

x 10−3

Response Confidence

Responses in coint(3) from a 1 std shock to epsilon(GDP_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−10

−8

−6

−4

−2

0

2

x 10−3

Response Confidence

Response in β3 to εiown Response in β3 to εi3m Response in β3 to εSCB−GB Responses in coint(3) from a 1 std shock to epsilon(OWN

M2M) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−4

−3

−2

−1

0

1

2

3

4

x 10−3

Response Confidence

Responses in coint(3) from a 1 std shock to epsilon(TBILL_3M_CURP) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−12

−10

−8

−6

−4

−2

0

2

x 10−3

Response Confidence

Responses in coint(3) from a 1 std shock to epsilon(SPREAD_CB_GB) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.005

0

0.005

0.01

0.015

0.02

0.025

0.03 Response Confidence

Response in the AD/credit relation cointegration relation (β3)Response in β4 to εh Response in β4 to εk Response in β4 to εm Response in β4 to εy

Responses in coint(4) from a 1 std shock to epsilon(HOUSEP_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.025

−0.02

−0.015

−0.01

−0.005

0

0.005 Response Confidence

Responses in coint(4) from a 1 std shock to epsilon(KREDIT_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−12

−10

−8

−6

−4

−2

0

2

4

6

8

x 10−3

Response Confidence

Responses in coint(4) from a 1 std shock to epsilon(M2M_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.01

−0.005

0

0.005

0.01

0.015

0.02 Response Confidence

Responses in coint(4) from a 1 std shock to epsilon(GDP_FP_LOG) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.025

−0.02

−0.015

−0.01

−0.005

0

0.005 Response Confidence

Response in β3 to εiown Response in β3 to εi3m Response in β3 to εSCB−GB Responses in coint(4) from a 1 std shock to epsilon(OWN

M2M) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.035

−0.03

−0.025

−0.02

−0.015

−0.01

−0.005

0

0.005 Response Confidence

Responses in coint(4) from a 1 std shock to epsilon(TBILL_3M_CURP) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.07

−0.06

−0.05

−0.04

−0.03

−0.02

−0.01

0

0.01 Response Confidence

Responses in coint(4) from a 1 std shock to epsilon(SPREAD_CB_GB) with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.01

0

0.01

0.02

0.03

0.04

0.05 Response Confidence

Note: i) Impulse-response for each cointegration relation to a positive one-std. shock to one of the errorterms of the reduced-form model (ε) ii) The gray bands are 95 % confidence bounds calculated bybootstrap-simulation with 500 replications.

45

SIR functions

htktmtyt

iown,ti3m,tcst

=

0.00−1

0.00(−2.52)

0.00(−8.97)

0∗

0∗

0∗

0∗

0.00(−2.39)

0.00(−2.28)

0.00(−8.97)

0∗

0∗

0∗

0∗

0.00(−5.29)

0.00(−4.08)

0.00(−8.97)

0∗

0∗

0∗

0∗

0.00(−5.83)

0

0

0 0 0 0

0.03−1.12

0.08(−4.15)

−0.01(−8.97)

0 0 0 0

0.02−0.97

0.07(−4.35)

0

0 0 0 0

0.00−1

0.00(−2.52)

0.00(−8.97)

..0.. ..0.. ..0.. ..0..

︸ ︷︷ ︸

C

∑ti=1 up1[ys],i

∑ti=1 up2[MP],i

∑ti=1 up3[k],i

∑ti=1 ut4[m],i

∑ti=1 ut5[cs],i

∑ti=1 ut6[iown],i

∑ti=1 ut7[h],i

(D.1)

+C∗(L)

0.00−0.22

0.00−0.4

0.00(−4.4)

0.00(−7.67)

0.00−1.81

0.00−0.42

0.00(−6.97)

0.00(−2.42)

0.00−1.42

0.00(−4.81)

0.00(−4.36)

0.00(−6.57)

0.00−0.57

0.00(−2.07)

0.00(−4.02)

0.00−1.2

0.00−1.33

0.00(−14.24)

0

0

0

0.00(−6.52)

0.00−1.46

0.00−0.06

0.00−0.27

0.00(−3.04)

0.00−0.04

0.00(−3.95)

0.01(−2.75)

0.02(−5.96)

0.00−1.28

0.00−1.32

0.00−0.48

0.02(−6.25)

0

0.05−1.14

0.13(−7.69)

−0.03−1.8

0.02−1.17

−0.01−0.46

−0.07(−4.48)

0.02−0.9

0.00−1.9

0.00(−2.24)

0.00(−5.45)

0.00(−2.04)

0.00(−6.28)

0

0

︸ ︷︷ ︸

B−1

up1[ys],tup2[MP],tup3[k],tut4[m],tut5[cs],tut6[iown],tut7[h],t

+ X0

46

Figure D.6: Impulse response functions to the permanent structural shocksResponse in h to up1[ys] Response in h to up2[MP] Response in h to up3[k]

Responses in HOUSEP_FP_LOG from a unit shock to s_1 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−4

−3

−2

−1

0

1

2

3

x 10−3

Response Confidence

Responses in HOUSEP_FP_LOG from a unit shock to s_2 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−1

0

1

2

3

4

5

6

x 10−3

Response Confidence

Responses in HOUSEP_FP_LOG from a unit shock to s_3 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−3

−2

−1

0

1

2

3

4

5

6

x 10−3

Response Confidence

Response in k to up1[ys] Response in k to up2[MP] Response in k to up3[k] Responses in KREDIT_FP_LOG from a unit shock to s_1 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−2

−1

0

1

2

3

4

5

x 10−3

Response Confidence

Responses in KREDIT_FP_LOG from a unit shock to s_2 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−4

−3

−2

−1

0

1

2

x 10−3

Response Confidence

Responses in KREDIT_FP_LOG from a unit shock to s_3 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.5

0

0.5

1

1.5

2

2.5

3

3.5

x 10−3

Response Confidence

Response in m to up1[ys] Response in m to up2[MP] Response in m to up3[k] Responses in M2M_FP_LOG from a unit shock to s_1 with95 % confidence region

Month0 5 10 15 20 25 30 35 40

−2

0

2

4

6

8

10

x 10−3

Response Confidence

Responses in M2M_FP_LOG from a unit shock to s_2 with95 % confidence region

Month0 5 10 15 20 25 30 35 40

−2

0

2

4

6

8

10

x 10−3

Response Confidence

Responses in M2M_FP_LOG from a unit shock to s_3 with95 % confidence region

Month0 5 10 15 20 25 30 35 40

−5

−4

−3

−2

−1

0

1

x 10−3

Response Confidence

Response in y to up1[ys] Response in y to up2[MP] Response in y to up3[k] Responses in GDP_FP_LOG from a unit shock to s_1 with95 % confidence region

Month0 5 10 15 20 25 30 35 40

−1

0

1

2

3

4

5

6

7

x 10−3

Response Confidence

Responses in GDP_FP_LOG from a unit shock to s_2 with95 % confidence region

Month0 5 10 15 20 25 30 35 40

−3

−2.5

−2

−1.5

−1

−0.5

0

0.5

1

1.5

x 10−3

Response Confidence

Responses in GDP_FP_LOG from a unit shock to s_3 with95 % confidence region

Month0 5 10 15 20 25 30 35 40

−1

−0.5

0

0.5

1

1.5

x 10−3

Response Confidence

Response in iown to up1[ys] Response in iown to up2[MP] Response in iown to up3[k] Responses in OWN

M2M from a unit shock to s

1 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.1

−0.08

−0.06

−0.04

−0.02

0

0.02

0.04

0.06 Response Confidence

Responses in OWNM2M

from a unit shock to s2 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.15

−0.1

−0.05

0

0.05

0.1Response Confidence

Responses in OWNM2M

from a unit shock to s3 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.02

−0.01

0

0.01

0.02

0.03

0.04

0.05 Response Confidence

Response in i3m to up1[ys] Response in i3m to up2[MP] Response in i3m to up3[k] Responses in TBILL_3M_CURP from a unit shock to s_1 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.2

−0.15

−0.1

−0.05

0

0.05

0.1

0.15 Response Confidence

Responses in TBILL_3M_CURP from a unit shock to s_2 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.4

−0.3

−0.2

−0.1

0

0.1

0.2 Response Confidence

Responses in TBILL_3M_CURP from a unit shock to s_3 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14 Response Confidence

Response in cs to up1[ys] Response in cs to up2[MP] Response in cs to up3[k] Responses in SPREAD_CB_GB from a unit shock to s_1 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−20

−15

−10

−5

0

5

x 10−4

Response Confidence

Responses in SPREAD_CB_GB from a unit shock to s_2 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−5

0

5

10

15

x 10−4

Response Confidence

Responses in SPREAD_CB_GB from a unit shock to s_3 with 95 % confidence region

Month0 5 10 15 20 25 30 35 40

−1.5

−1

−0.5

0

0.5

1x 10

−3

Response Confidence

Note: i) Impulse-response for each individual variable to a positive structural shock of one std. ii) Thegray bands are 95 % confidence bounds calculated by bootstrap-simulation with 500 replications.

47