Modeling Transmission of Oil Price Shocks in a Data

Rich Environment

Knut Are Aastveit† ∗

† University of Oslo and Norges Bank

This version: March 31, 2009

Draft - please do not quote

Abstract

This paper studies the impact of different types of oil price shocks on the U.S.

economy using a factor-augmented vector autoregression (FAVAR) approach. This

framework allows us to study the transmission of oil price shocks to a large amount

of U.S. macroeconomic variables as well as the interaction between these shocks

and monetary policy. We show that oil demand shocks are more important than oil

supply shocks as a driving force behind several macroeconomic variables. Further,

we show that the origin of the oil demand shocks is important. The U.S. economy

responds differently to a shock to global demand for industrial commodities than

to a demand shock that is specific to the global crude oil market. Finally, we show

that monetary policy reacts differently to the different oil price shocks.

Keywords: Oil demand shocks, Oil supply shocks, Business cycle, Monetary pol-

icy, Factor model, FAVAR

JEL Classification: C3, E31, E32, E4, E5, Q43

∗I have received helpful comments from Hilde C. Bjørnland and Ragnar Nymoen as well as seminar

participants at the University of Oslo. E-mail: k.a.aastveit@econ.uio.no.

1 Introduction

The common approach in studies of oil price shocks, is to evaluate the response of macroe-

conomic variables to exogenous changes in the price of oil. An implicit assumption is

that oil price innovations are equated with oil supply shocks. Recently this view has

been challenged by among others Barsky and Kilian (2002), Barsky and Kilian (2004)

and Kilian (2009). The price of oil is, as any other price, driven by both demand and

supply shocks. These shocks may have different effect on the dynamics of the real oil

price and hence also the macro economy. Kilian (2009) proposes a structural VAR model

for the global crude oil market and its interaction with global demand for commodities.

He identifies three different shocks to the global crude oil market; a crude oil supply

shock, a shock to the global demand for all industrial commodities and a demand shock

that is specific to the global crude oil market. His results suggest that the implications

of higher oil prices for U.S. real GDP and CPI inflation depend on the cause of the oil

price increase. While his model is important for understanding the effect of real oil price

movements from the different shocks to the oil market, a shortcoming is that he neglects

the interaction between the oil market, the U.S. macro economy and monetary policy.1

This is important for understanding the transmission of different shocks to the crude oil

market on the macro economy.

Oil price shocks pose a difficult challenge to policy makers by balancing the trade-off

between higher inflation and higher unemployment. Bernanke, Gertler, and Waston (1997)

and Bernanke, Gertler, and Watson (2004) suggest that monetary policy makers have his-

torically leaned toward keeping inflation low at the cost of greater slowdown in economic

activity. That is, the systematic component of monetary policy accounted for a large por-

tion of the decline in GDP growth following an oil price shock. This view was challenged

by Hamilton and Herrera (2004) and more recently by Bachmeier (2008) and is still a

matter of debate. However, the models in these papers are still limited. In particular

they do not distinguish between the response of monetary policy to different type of oil

price shocks.

1His model captures only the interaction between the oil market and the global demand for industrial

commodities.

1

In this paper, we suggest a framework that address all the issues in the papers men-

tioned above. In addition our framework gives details on the transmission mechanism for

a large amount of both aggregated and disaggregated U.S. macroeconomic variables to

different shocks in the oil market. This helps us understand how the U.S. macro econ-

omy reacts to different shocks in the oil market, accounting for the response from the

Federal Reserve. Further, our framework can also clarify whether the shocks identified

in Kilian (2009) really can be interpreted as supply and demand shocks with respect

to the U.S. macro economy. Our framework builds on the factor-augmented vector au-

toregression (FAVAR) approach, proposed by Bernanke, Boivin, and Eliasz (2005). This

framework allows us in a very simple way to study the transmission of different shocks

to the oil market to a large amount of variables. Our data set is similar to the one used

in Boivin, Giannoni, and Mihov (2009). It contains 656 monthly U.S. macroeconomic

variables over the sample period 1976M1 - 2005M6.

We find considerable differences in the response of both nominal and real variables to

the different oil price shocks. First, we show that a positive oil-specific demand shocks

increase the real price of oil and consumer prices and have a negative effect on the real

economy. Hence, it indicates that such shocks yield the well known trade-off between

higher unemployment and inflation often associated with supply shocks. This suggests,

that if the oil-specific demand shocks identified in our model truly are demand shocks

to the oil market, they have an effect on the macro economy that is associated with a

supply shock. We show that the Fed funds rate decreases after a oil-specific demand

shock. This indicates that the Fed have leaned towards stabilizing the real economy

rather than stabilizing inflation.

Second, we find that positive shocks to the global demand in commodity markets have

a large and persistent positive effect on both the real price of oil and consumer prices.

This causes a monetary tightening on impact. The effect on the U.S. real economy is

slightly positive during the first year. The positive effect is reversed after a year and

becomes significantly negative after about 2 years. In other words, shocks to the global

demand for commodities that increase the real price of oil do also have a negative effect

on the U.S. economy. In contrast to other oil price shocks, the negative effect on the real

economy is delayed.

2

Third, the effect of a negative oil supply shock on the U.S. economy is rather small.

It has a weak negative effect on the real economy while prices are almost unaffected.

In fact, there are indications of falling consumer prices, while producer prices increase

weakly. The effect on the Fed funds rate is negligible. To the best of our knowledge this

is the first paper to study the effect of different type of oil price shocks on a wide range

of U.S. macroeconomic variables. It is also the first paper to study the effect of different

oil price shocks applying a FAVAR framework.2

There is a large literature that studies the effect of oil prices on the macro economy.

In a classic paper, Hamilton (1983) showed that all US recessions but one since World

War II were preceded by a spike in the oil prices. Subsequent to Hamilton’s work, a large

body of research have suggested that oil price variations have strong and negative impact

on oil importing countries, see for instance Bjørnland (2000) and Jimenez-Rodriguez and

Sanchez (2005).

The oil price - macroeconomic relationship seemed to loose its significance from the

mid 1980s. As a result several studies have focused on the asymmetric role of oil price

shocks on the economy. Mork (1989), Hamilton (1996) and Hamilton (2003) find that

by assuming asymmetric or non-linear transformations of oil prices, the negative link

between oil price increases and economic activity still prevails. An alternative explanation

for the asymmetric role of oil prices is that the underlying shock to oil prices matters,

as suggested in Kilian (2009). This paper focus on the latter explanation and is the first

paper to study in detail the transmission of different type of oil price shocks on the U.S.

macro economy.3

Finally, oil price shocks are one of the candidates for explaining the great moderation,

both in terms of the ”smaller shocks” hypothesis and the ”better policy” hypothesis.

Recently, there has been several studies that tries to investigate whether oil price shocks

and monetary policy can explain the great moderation, see Blanchard and Gali (2007),

2Boivin, Giannoni, and Mojon (2008) studies the effect of an exogenous oil supply shock on the Euro

area economy.3The effect of oil supply shocks have been studied extensively in the literature. Recent research by

Kilian (2008a) and Kilian (2008b) have documented that oil supply shocks (measured as disruption in

global crude oil production) cannot alone explain the main bulk of oil price fluctuations. His results also

suggest that this type of shocks do not have substantial effect on real growth in any of the G7 countries.

3

Baumeister and Peersman (2008) and Herrera and Pesavento (2009) among others. Typ-

ically, these studies suggest that both the importance of oil price shocks as well as the re-

sponse from monetary policy and macroeconomic variables to these shocks have changed.

To understand the role of shocks to the oil market for the great moderation, it is first

crucial to understand how the monetary authorities reacts to the different type of oil

shocks. Second, it is crucial whether the importance of these oil shocks have changed

over time.

This paper is organized as follows: In the following section we present the FAVAR

model. Section 3 presents our data and discuss model selection issues. Empirical results

are discussed in section 4, while robustness results for various data and model specifica-

tions are presented in section 5. Finally section 6 summarizes and concludes.

2 Model

The empirical framework that we consider is based on the factor-augmented vector au-

toregression model (FAVAR) described in Bernanke, Boivin, and Eliasz (2005). One of

its key features is to provide estimates of the macroeconomic factors that affect the data

of interest by systematically and consistently exploiting all information from a large set

of economic indicators, see Boivin, Giannoni, and Mihov (2009). In our application,

we will estimate the empirical model by exploiting information from a large number of

macroeconomic indicators, as well as from disaggregated data. The framework allows us

to characterize the response of all data series to macroeconomic disturbances, such as

different oil price shocks and monetary policy shocks.

Assume that the state of the economy is captured by a few common components, de-

noted Ct. We are interested in characterizing the effect of different oil price shocks as well

as monetary policy shocks on the macro economy. Hence, we include observable variables

that we can relate to these shocks. For the global oil economy we follow Kilian (2009) and

include 3 observable variables, the percent change in global crude oil production (Δprodt),

an index of the real economic activity that drives demand for industrial commodities in

4

global industrial commodity markets (reat)4 and the real price of oil (rpot)

5. In addition

we include the Federal funds rate (Rt) as the observable measure of the monetary policy

stance. These variables are assumed to have pervasive effects throughout the economy

and will thus be considered as common components of all variables entering the data set.

In addition we extract some unobservable common factors (Ft) from a large data set to

include in Ct. We assume that the dynamics of the common components is modeled as a

VAR and given by

Ct = Φ (L) Ct−1 + ut (1)

where

Ct =

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

Δprodt

reat

rpot

Ft

Rt

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

, (2)

and Φ (L) is a conformable lag polynomial of finite order. The error term ut is assumed

to be i.i.d with mean zero. The system (1) is a VAR in Ct. The additional difficulty, with

respect to a standard VAR is that the factors Ft, which is a vector of dimension K × 1,

are unobservable. These factors are extracted from a large number of macroeconomic

variables Xt of dimension N×1. We assume that Xt can be described by an approximate

dynamic factor model given by

Xt = ΛCt + et, (3)

where Λ is a N × (K + 4) matrix of factor loadings and et is a vector of series-specific

components that are uncorrelated with the common component Ct. The series-specific

4This is the index developed by Kilian (2009). It is based on dry cargo single ocean freight rates

and is explicitly designed to capture shifts in demand for industrial commodities driven by the global

business cycles. See Kilian (2009) for more details about the index.5The real oil price series is obtained based on the U.S. refiner acquisition cost of imported crude oil.

The nominal oil price has been deflated by the U.S. consumer price index.

5

components are allowed to be serially correlated and weakly correlated across indicators.6

However, note that in contrast to a standard approximate dynamic factor model, we

assume that some of the factors are observable.

2.1 Estimation

We estimate the model by using a two-step principal component approach similar to

Bernanke, Boivin, and Eliasz (2005) and Boivin, Giannoni, and Mihov (2009). In the

first step, we extract principal components from the large data set Xt, to obtain consis-

tent estimates of the common factors. In a second step, we add the three variables from

the global crude oil market and the Federal funds rate and estimate a structural VAR.

In this step we impose the constraint that the three oil variables and the Federal funds

rate are four of the factors in the first-step estimation. This guarantees the estimated

latent factors to recover dimensions of the common dynamics not captured by the global

oil economy and the Federal funds rate. More specifically, we follow the iteration proce-

dure used in Boivin and Giannoni (2008) and Boivin, Giannoni, and Mihov (2009). We

start with an initial estimate of Ft, denoted by F(0)t and obtained as the K first principal

components of Xt. We then iterate through the following steps:

(i) Regress Xt on F(0)t and the observed factors Yt = [Δprodt, reat, rpot, Rt]

′. and ob-

tain λ(0)Y .

(ii) Compute X(0)t = Xt − λ

′(0)Y Yt.

(iii) Estimate F(1)t as the first K principal components of X

(0)t .

(iv) Repeat the procedure multiple times.

Having estimated the factors Ft and the factor loadings Λ, we can now estimate the

VAR in equation (1) with OLS and then seek a more structural representation of the

system.

6The model is an approximate factor model, since in contrast to the strict factor model, the idiosyn-

cratic terms in equation 3 are allowed to be weakly correlated. See Chamberlain and Rothschild (1983),

Forni et al. (2000) and Stock and Watson (2002a) for details.

6

3 Data and Model specification

3.1 Data

We use a balanced panel of 656 monthly series for the U.S. economy.7 The sample

period is from 1976M1 - 2005M6. The data set includes 110 macroeconomic indicators

and covers a broad specter of the U.S. economy and include different series for prices,

industrial production, unemployment, stock prices and interest rates among others. The

variables are mostly similar to the ones used in Stock and Watson (2002a) and Bernanke,

Boivin, and Eliasz (2005).

In addition we follow Boivin, Giannoni, and Mihov (2009) and include disaggregated

data published by the Bureau of Economic Analysis on personal consumption expendi-

ture.8 We include 194 disaggregated PCE price series and the corresponding consumption

series. Finally in order to get a more detailed picture of the characteristics of the price

responses we include 154 producer price series. The choice of starting data is limited due

to the inclusion of the disaggregated price series.9 There are two reasons for including the

additional disaggregated price series. First, the disaggregated price data provide useful

information for the estimation of the monetary policy shock. This is shown in Boivin,

Giannoni, and Mihov (2009). Second, we are interested in the responses of some of the

disaggregated price series to the different oil shocks. In particular, to price series related

to oil intensive sectors. The 652 macroeconomic series and disaggregated price series are

collected in Xt. All series are then initially transformed to induce stationarity.10 In addi-

tion we include the Federal funds rate, the percent change in global crude oil production,

an index of the real economic activity that drives demand for industrial commodities in

global industrial commodity markets and the real price of oil. The description of all the

series in the data set and their transformation are described in Appendix C.

7The choice of data to include in Xt might be important. In theory more data are always better, see

Stock and Watson (2002b). However, in practice this not necessarily the case. Boivin and Ng (2006)

provide examples where adding more data has perverse effects.8The data set is available at Marc Giannoni’s web page.9We could have started the analysis 2 years earlier by excluding the disaggregated price series.

Monthly data on oil production is first available from 1974.10This is crucial when estimating the factors with principal components.

7

3.2 Identification

The system (1) is a VAR in Ct. The additional difficulty, with respect to standard

VARs, is that the factors Ft are unobservable. However, once the factors are consistently

estimated by principal components, we have a standard VAR framework. In this paper

we are interested in studying the effect of different oil price shocks on the U.S. macro

economy and its interaction with monetary policy.

The residuals from equation (1) are correlated and cannot be interpreted as structural

shocks. The solution of equation (1) has the following moving average representation

Ct = B (L) ut, (4)

Assume that the underlying orthogonal structural disturbances (εt) can be written as

linear combinations of the innovations (ut), i.e. ut = Sεt, where S is a ((K+4)×(K+4))

contemporaneous matrix. Equation (4) can then be written as

Ct = B (L) Sεt = D(L)εt (5)

where B(L)S = D(L).

To orthogonalise the shocks we follow the standard literature and order the vector of

shocks recursively by using Cholesky decomposition. That is, we choose an ordering for

the variables in the system that only allows for a contemporaneous correlation between

certain series. Specifically, we assume the following recursive identifying restrictions

Ct =

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

Δprodt

reat

rpot

Ft

Rt

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

= B (L)

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

S11 0 0 0 0

S21 S22 0 0 0

S31 S32 S33 0 0

S41 S42 S43 S44 0

S51 S52 S53 S54 S55

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

εOSt

εGDt

εODt

εFt

εMPt

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(6)

First, the model postulates a vertical short-run supply curve of crude oil. World

crude oil production does not respond within the month to demand shocks in the crude

oil market, shocks to the U.S. macroeconomic factors or U.S. monetary policy. Second,

oil-specific demand shocks, U.S. macroeconomic factors and U.S. monetary policy do

8

not affect the business cycles in global industrial commodity markets within the month.

Third, oil prices are not affected contemporaneously by U.S. macroeconomic factors or

U.S. monetary policy. Fourth, U.S. macroeconomic factors are not allowed to react

contemporaneously to U.S. monetary policy. Finally, the Federal funds rate is allowed to

react contemporaneously to all variables included in Ct. Note that the restrictions above

only concerns contemporaneous relations. After one period (one month), all variables

can react to all the shocks. Further, the model also allows us to impose and test over-

identifying restrictions on the Λ matrix.

3.3 Model Specification

When using factor models, the number of factors is usually exogenously determined.11

The data set we use to extract the factors is similar to the one used in Boivin, Giannoni,

and Mihov (2009). In addition we assume three additional observable factors related to

the oil market. Potentially, one or more of these observable factors can be heavily related

to the unobservable factors in Boivin, Giannoni, and Mihov (2009). We choose the same

number of factors, K = 5, as in their paper. This will guarantee that our factors at least

span the same space as the factors in Boivin, Giannoni, and Mihov (2009). In addition,

we check for robustness of other factor combinations.

We estimate the system (1) and (3) for the period 1976M1 - 2005M6 using the data

described above and assuming 5 latent factors in the vector Ft. We use 13 lags in esti-

mating equation (1).12 Information about the fit of the model and some model statistics

are given in Appendix B.

3.4 Co-movements between US variables and common factors

We first investigate the degree of correlation between U.S. macroeconomic variables and

the U.S. factors and the three variables related the oil market. Table A-5 in Appendix

11Bai and Ng (2002) provide an information criterium to determine the number of factors present a

large data set. However, this does not necessarily address the question of how many factors should be

included in the VAR.12We applied a F-test for model reduction. We could not reject the hypothesis of reducing the model

to 12 lags at a 5 percent significance level.

9

F, shows the correlation between the the unobservable and observable common factors

in equation (2). The unobservable factors are uncorrelated by construction. The table

shows that the oil related factors and the Fed funds rate are correlated both among each

other and with the unobservable factors. In this paper, we are in particular interested

in the effect of different oil shocks on the U.S. macro economy. Before turning to the

analysis of the FAVAR model, we check more formally whether oil factors can predict

U.S. macroeconomic variables (represented with U.S. factors and the Fed funds rate).

We run a Granger causality test, see Granger (1969). The results are given in table

A-3 in Appendix B. We test whether the lags of all oil factors jointly have predictive

power for the current values of U.S. factors Ft and the Fed funds rate. Under the null

hypothesis, the oil factors have no predictive power. The results suggests that all U.S.

common factors as well as the Fed funds rate are Granger caused by the oil factors at

the 1 percent level.

The U.S. common factors, Ft are unobservable and do not have a clear economic in-

terpretation. However, the correlations between all the variables in Xt and each of the

U.S. common factors can give an indication of what these unobservable factors repre-

sents. The correlation between all the 110 macroeconomic indicators and each of the

common factors in Ct (both observable and unobservable), are given in Appendix F.13

The table indicates that the first U.S. factor is strongly correlated with nominal variables.

In particular, it is strongly correlated with different measures of interest rates and con-

sumer prices. The second factor is strongly related to employment measures and housing

measures and to some extent production measures. The third factor is clearly related to

different measures of industrial production and hours worked, while the fourth factor is

related to measures of unemployment. The fifth factor seems to also be related to the

real economy, but is far less correlated with these measures than the second, third and

fourth factor. Finally, both the Fed funds rate and the real price of oil seem to be highly

correlated with measures of interest rates and consumer prices, while oil production has

a rather low correlation with most U.S. macroeconomic variables.

Table A-2 shows the fraction of volatility for a selection of key macroeconomic vari-

13The correlation between the disaggregated price series and the common factors are available upon

request. They are not included in this version of the paper to save space.

10

ables explained by the 5 U.S. factors and the Fed funds rate, the 3 factors from the oil

market and all the factors together (Ct). This corresponds to the R2 statistics obtained

from the regression of equation (1) for the full sample. The last column shows that U.S.

macroeconomic variables are on average strongly correlated with the common factors. On

average, all factors explain about 50 percent of the variance in the U.S. macroeconomic

series and about 25 percent of the total variance in all 652 disaggregated data series.14

Most of the real variables, like industrial production and unemployment as well nominal

variables such as aggregate price series, have a R2 in the region between 0.6 and 0.9.

Financial variables, such as exchange rates and stock prices are on the other hand not

very well explained by the common factors. A large fraction of the variability in these

series are explained by their idiosyncratic terms.

The first column shows that most of the common fluctuations in U.S. macroeconomic

series are determined by the common U.S. factors (including the Fed funds rate). In

fact, the R2 obtained for most of these variables by regressing them on all factors are not

much higher than those found by regressing them only on the U.S. factors. The second

column shows that the oil factors explain parts of the the variability in several of the U.S.

macroeconomic variables. In particular measures for prices and unemployment. However,

note that the sum of the R2 measures in column 1 and column 2, does not correspond

to the fraction of the variance explained jointly by both sets of factors (third column).

The reason is that the U.S. factors and oil factors are allowed to be correlated. In most

cases the oil factors do not seem to explain much more of the variability than already

explained by the U.S. factors. This suggests that the U.S. factors and the oil factors that

explain these variables are correlated. This is confirmed in table A-5 in Appendix F.

4 Results

One of the advantages of using a FAVAR model is that we can analyze the responses

to a large number of variables to different structural shocks, with minimal identifying

restrictions. In particular we can calculate impulse responses to all variables included in

14The reason for this rather low number is that the variation in most of the disaggregated price series

are mainly explained by their idiosyncratic component.

11

Xt. Note that for each variable in Xt, equation (3) implies that

xit = Λ′iCt + eit, (7)

where xit is a random variable in Xt. This formulation implies that each variable in Xt is

allowed to react contemporaneously to all structural shocks despite the recursive ordering

in equation (1). In this way, for instance financial variables included in Xt are allowed to

react contemporaneously to changes in the Federal funds rate. This makes the FAVAR

model very flexible.

4.1 Effects of oil shocks

In the following we will study the transmission of different oil price shocks on the U.S.

macro economy. We will in particular focus on the response of the following six variables:

oil production, an indicator for global demand for commodities, real price of oil, indus-

trial production, the consumer price index and the federal funds rate. We will compare

the impulse responses from our chosen FAVAR model with two other models. First, a

standard monetary SVAR model with oil. That is a SVAR model with the following vari-

ables Yt = [Δprodt, reat, rpot, IPt, CPIt, Rt]′, where IPt denotes industrial production

and CPIt denotes the consumer price index. We use the same recursive identification as

in the FAVAR model and denote this as the SVAR model.

Second, for the three oil related variables, we will also compare the responses with

a version of the model in Kilian (2009).15 That is a SVAR model with the 3 variables

Δprodt, reat, rpot. We denote this as the Kilian model. In addition we will also use the

FAVAR model to produce impulse responses for a few other selected variables of interest.

All shocks are normalized to have a positive effect on the real price of oil and represents

a one standard deviation shock of the present structural shock in the Kilian model. The

structural shocks identified from the FAVAR model are plotted in Figure A-1 in Appendix

D. The shape of the shocks supports the idea in Kilian (2009) that there has been a shift

in the relative importance of oil supply shocks towards oil demand shocks. The timing

15Note that this model differ slightly from the model specified in Kilian (2009) as we use 13 lags

instead of 24 lags. Our sample is also slightly different as Kilian (2009) use data from 1973M1 to

1976M10. However, the results are almost similar.

12

of this shifts coincides with the start of the Great moderation. We will discuss this more

later.

4.1.1 Oil supply shock

Figure 1 shows the responses of global oil production, real economic activity, the real price

of oil, industrial production, consumer prices and the Fed funds rate to one-standard

deviation structural shock of the Kilian model. An unexpected oil supply disruption

causes a sharp decline in oil production upon impact, followed by a partial but slow

reversal within the next years. This shock triggers a transitory increase in the real

price of oil as well as a partial reduction of global real economic activity. Figure A-3

in Appendix D shows that the first effect is significant, while the latter is insignificant.

Further, the shock causes a temporary decline in industrial production during the first 2

years after the shock. It also causes a weak but persistent reduction in consumer prices.

The negative response of consumer prices is somewhat puzzling and do not coincide

with what is normally associated as a negative supply shock. However, the effect is not

significance.16 The monetary policy authorities reacts to the negative oil supply shock

by decreasing interest rates. The effect is however weak and insignificant.

Figure 1 shows some few differences between the responses from the FAVAR model,

the SVAR model and the Kilian model. First, the Kilian model seems to yield a more

persistent response for both the real price of oil and the global oil production than what

is evident in the two other models. However, the differences are quite small. Second,

the response of industrial production is different in the FAVAR model and in the SVAR

model. In particular, the FAVAR model indicates a more rapid and stronger slowdown

of the economy than what is evident in the SVAR model. The response of the Federal

funds rate is also different for the first 6-12 months as well as the response of consumer

prices on longer horizons. That is after 3 years and onwards.

Figure 2 shows the impulse responses of a selection of key macroeconomic variables

to a oil supply shock. The responses generally have expected signs. An unanticipated

reduction in oil supply cause real activity measures to decline on impact. The unemploy-

ment rate significantly increases, while employment measures decrease. The latter effect

16Kilian (2009) finds the similar result.

13

Figure 1: Response to a one standard deviation shock to global oil production.

is however insignificant. Further, the dollar appreciate and the effect on the stock market

is negative. The Commodity price index is somewhat surprisingly reduced on impact.

However, the effect is insignificant and is reversed after about 1 year.

Table A-1 in Appendix A shows the variance decomposition of all shocks for the

variables of interest. The table shows that oil supply shocks are first of all an important

driving force behind variation in oil production itself. However, these shocks also explain

about 5-8 percent of the variation in the real price of oil and the unemployment rate

after 1-2 year and 3-5 percent of variation in consumer prices, industrial production,

employment, commodity prices and stock prices. The contribution of oil supply shocks

to the variability in the Fed funds rate is almost negligible.

4.1.2 Aggregate demand shock in global commodity markets

An unanticipated aggregate demand expansion in global commodity markets has a per-

sistent and positive effect on global real economic activity. Figure 3 shows that this shock

temporarily increases the production of world crude oil with a short delay. The effect is

14

Figure 2: Response to a one standard deviation shock to global oil production.

insignificant and reversed after about 18 months. An aggregate demand expansion causes

a large and persistent significant (see figure A-4 in Appendix D) increase in the real price

of oil with maximum effect after 1-2 years. Further, it stimulates the U.S. real economy

and hence causes a temporarily increase in industrial production and a persistent increase

in prices. This leads to a significant monetary tightening after 6 months which together

with the higher oil price reverse the positive effect on the U.S. real economy after about 1

year. This is somewhat surprising as the positive effect on the global commodity markets

is persistent. This indicates that the negative impact of higher oil prices is larger on the

U.S. economy than for other countries.

There are small differences in the impulse responses from the FAVAR model and the

SVAR model. However, the Kilian model seems again to yield a more persistent effect

on oil prices in addition to a sharp and long reduction in oil production starting 2 years

after the shock.

A global aggregate demand expansion cause real activity measures to temporarily

increase, se figure 4. The effect on the unemployment rate is significant for the first

months. The temporarily increase in real activity measures are then reversed after about

15

Figure 3: Response to a one standard deviation shock to aggregate demand in global

commodity markets.

1 year as interest rate and prices increase. The increase in the Federal funds rate is

significant after 6-12 months and the unemployment rate increases significantly after 2

years. Further, the shock has a positive effect on the U.S. stock market on impact and

leads to a depreciation of the dollar. The effects are insignificant though. The commodity

price index increases on impact as expected. The effect is significant for the first year.

Table A-1 in Appendix A shows that shocks to global demand for industrial com-

modities explains more than 10 percent of the variation in the unemployment rate and

commodity prices after one year and more than 20 percent after 3 years. Further, these

shocks explain about 10 percent of the variation in the Fed funds rate and the real price

of oil after one year and between 8 and 10 percent of the variation in oil production,

consumer prices, employment and industrial production on long horizons. That is after

2-3 years.

16

Figure 4: Response to a one standard deviation shock to aggregate demand in global

commodity markets.

4.1.3 Oil-specific demand shock

Unanticipated oil-specific demand shocks have an immediate, large and persistent signif-

icant effect on the real price of oil. This is shown in Figure 5. Kilian (2009) argues that

this type of shocks capture shifts in the price of oil driven by higher precautionary de-

mand associated with market concerns about the availability of future oil supplies. These

shocks are associated with a strong and temporarily significant (see figure A-5) increase in

global demand for commodities and a very short-run decline in oil production. The latter

effect is insignificant. The oil-specific demand shock has a strong and persistent positive

effect on consumer prices and a negative effect on industrial production. The first effect

is significant for the first year, while the latter effect is insignificant and reversed after

about 3 years. There is evidence of a monetary tightening after the positive oil-specific

demand shock. This indicates that the Fed funds rate reacts to both the price increase

and the slowdown in industrial production. The response is however insignificant.

There are some few differences between the responses from the FAVAR model, the

17

Figure 5: Response to a one standard deviation shock to oil-specific demand.

SVAR model and the Kilian model. First, the Kilian model seems to yield a stronger and

more persistent response for the real price of oil than what is evident in the two other

models. Second, the Kilian model indicates a strong and long lasting negative response

of oil production after about 2 years and onwards. This is not evident in the two other

models. Third, the response of industrial production for the first 2 years are different in

the FAVAR model and in the SVAR model. In particular, the FAVAR model indicates

a more rapid and stronger slowdown of the economy than what is evident in the SVAR

model. The opposite is the case for consumer prices, where the SVAR model yields a

stronger positive response than the FAVAR model for the first years. The response of the

Federal funds rate is also different. This is a result of the differences mentioned above.

The FAVAR model indicates a decrease in the Fed funds rate for the first years after a

shock to the real price of oil, while the SVAR model indicates a monetary tightening.

Figure 6 shows that a positive oil-specific demand shock cause real activity measures to

decrease. The effect is significant for the horizon of 6 to 18 months for the unemployment

rate and 6 to 12 months for employment. There is a strong significant increase in the

commodity price index for the first 6 months as well as for the measure of housing starts.

18

Figure 6: Response to a one standard deviation shock to oil-specific demand.

The latter effect is somewhat surprising. Further, a shock to the real price of oil has

a negative impact on consumer expectations where the effect is significant after about

6 months. Finally, the impact on stock prices is negative. This confirms the finding in

Kilian and Park (2007) that only higher oil prices caused by an oil-specific demand shock

cause lower stock prices.17 The effect is significant for the first 6 months.

All in all, an oil-specific demand shock has a negative impact on the U.S. economy,

although it increases the global demand for commodities. In particular, it has a negative

effect on the real economy and it increase prices. Hence, it causes the well known trade-off

between higher unemployment and higher inflation often associated with supply shocks.

This indicates, that if the oil-specific demand shock identified in our model really is a

demand shock to the oil market, it has an effect on the macro economy that is associated

17Several other papers have found that oil price increases have a negative effect on the stock market

for oil importing countries, see Jones and Kaul (1996), Sadorsky (1999) and Nandha and Faff (2008)

among others. On the other hand Bjørnland (2009) finds that stock prices has a positive effect on the

Norwegian stock market. Norway is an oil-exporting country. However, none of these papers distinguish

between different type of oil shocks.

19

with a supply shock.

The variance decomposition in table A-1 shows that oil-specific demand shocks are

an important driving force behind several of the macroeconomic variables of interest. It

explains more than 15 percent of the variation in consumer prices, producer prices and

commodity prices both in the short and the long run as well as more than 10 percent of

the variation in the unemployment rate for all horizons after the first 2 months. Further,

it contributes to more than 15 percent of the variation in stock prices, exchange rates

and housing starts at most horizons and just below 10 percent of the variation in global

demand for commodities and consumer expectations. After 1 year oil specific demand

shocks contribute to 5 percent of the variation in Fed funds rate and about 8 percent of

the variation in industrial production and employment. The contribution of oil-specific

demand shocks to other variables are more modest.

4.2 Monetary Policy Shock

This paper focus on the transmission of different oil shocks on the U.S. macro economy

and its interaction with monetary policy. For completeness we will in this section briefly

discuss and show the effects of a monetary policy shock on the U.S. economy. This

has already been studied in detail within a FAVAR model by Bernanke, Boivin, and

Eliasz (2005) and Boivin, Giannoni, and Mihov (2009). Hence, we will mainly focus on

whether the inclusion of the oil market data affect the results in these papers.

Figure 7 and Figure 8 show the responses of different macroeconomic variables to

an unexpected increase in the federal funds rate of 25 basis point. As expected, a U.S.

monetary policy shock hardly has any affect on the oil market. There are positive (but

insignificant) movements in both the global demand for commodities and the real price

of oil, while oil production remains constant. Further, a monetary tightening has the

expected negative effect on industrial production and causes a decrease in prices. Note,

as highlighted in Bernanke, Boivin, and Eliasz (2005), that the FAVAR model removes

the price puzzle evident in most standard SVAR models. All in all the impulse responses

are similar to the ones in Bernanke, Boivin, and Eliasz (2005) and Boivin, Giannoni, and

Mihov (2009).

20

Figure 7: Response to a unexpected increase of 25 basis points in the Federal funds rate.

Table A-1 shows that the contribution of monetary policy is as expected. It explains

more than 10 percent of the variation in exchange rates as well as between 3-6 percent of

the variation in prices, the unemployment rate, industrial production and stock prices.

It has as expected negligible effects on global oil production, the global demand for

commodities and the real price of oil.

5 Results of robustness tests

We check robustness in 6 ways. First, we check the robustness of our results with respect

to the number of factors included in the FAVAR. Second, we check for robustness with

respect to the lag length in the VAR in equation (1). Third, we check for robustness

with respect to starting the estimation in 1984. Then we check for robustness with

respect to both alternative identification of the VAR (equation (1)) as well as alternative

transformation of the real price of oil. Finally, we check for robustness with respect to

extracting factors from a smaller (subset of our) data set.

21

Figure 8: Response to a unexpected increase of 25 basis points in the Federal funds rate.

5.1 Different number of factors

The figures in Appendix E.1 compare the impulse responses for several variables to dif-

ferent oil shocks as well as monetary policy shocks choosing respectively 1, 3, 5 and 7

factors. The figures show that the results are robust to the number of factors for most of

the variables. There are however two observations worth mentioning. First, the results

when choosing 1 factor seem to differ from the others. This indicates that 1 factor is not

enough to capture the appropriate dimension of the U.S. economy. Second, for some of

the financial variables, the results differ when selecting 7 factors. The variability of these

variables were not very well explained in our preferred model with 5 factors. Hence, if

responses to financial variables are of particular interest, one should choose more than 5

factors.

5.2 Different lag length

The figures in Appendix E.2 compare the results for different number of lags in equa-

tion (1). Hamilton and Herrera (2004) criticized the results in Bernanke, Gertler, and

22

Waston (1997) for not being robust to the choice of lag length. Bernanke, Gertler, and

Waston (1997) specified a monthly VAR model with 7 lags using U.S. macroeconomic

data. Hamilton and Herrera (2004) showed that the econometric evidence favored a longer

lag length. For model specifications of 12 to 16 lags the results in Bernanke, Gertler, and

Waston (1997) do not longer hold. Several papers have shown that the the maximum

effect of oil shocks on macroeconomic variables lags with around 1 year. This indicates

that one should at least use 12 lags for monthly data. Kilian (2009) argues that the

dynamics is even more persistent and includes 24 lags in his monthly VAR model. We

check robustness of our results for respectively 7, 13, 18 and 24 lags. The figures show

that the results are very similar for the different choices of lag length. There are two

exceptions. First, there are some small differences in the response of industrial produc-

tion and employment to shocks in oil production and the real price of oil. Second, the

response of financial variables differ for the different lag specifications.

5.3 Post 1984

Recent research has provided evidence of widespread instability in many macroeconomic

series,18 of changes in monetary policy behavior19 over our sample (1976M1 - 2005M6),

and of an important reduction in output volatility (the great moderation) since around

1984. Herrera and Pesavento (2009) show that the macroeconomic response of an exoge-

nous oil price shock as well as the response from monetary policy to such a shock has

changed after 1984. Baumeister and Peersman (2008) use a Bayesian time varying VAR

similar to Primiceri (2005) and show that the effect on U.S. GDP and inflation from oil

demand and oil supply shocks have changed over time. In particular, the effects have

changed since the mid 1980s.20

We check whether our results are robust to all these events. The figures in Appendix

E.3 show impulse responses for different variables to the 4 structural shocks for the

18Stock and Watson (1996) and Stock and Watson (2002b) have provided evidence of instability in

VARs.19See for instance Bernanke and Mihov (1998), Clarida, Gali, and Gertler (2000) and Boivin and

Giannoni (2006)20They identify oil demand and oil supply shocks using sign restrictions. They apply their identification

scheme to U.S. quarterly data.

23

sample period 1984M1 - 2005M6. The figures indicate some quantitatively differences

but qualitatively the results are almost the same. However, there are two differences

that are worth mentioning. First, the negative effect on the real economy after a shock

to oil production and a shock to the real price of oil is larger in the post 1984 sample.

As a consequence, the response of the Fed funds rate after such shocks is also larger.

Further, prices react more modestly after a shock to the real price of oil in the post

1984 sample. Second, the effects of a monetary policy shock are different for some of the

variables of interest. In particular, the response of industrial production and employment

are stronger, while the response of prices are much smaller. In fact there is evidence of a

very weak price puzzle. This is in line with results in Boivin, Giannoni, and Mihov (2009).

5.4 Alternative identification

The rather high correlation between the real price of oil and the index of global demand

for commodities with some of the U.S. factors might indicate strong simultaneity. Hence,

we check for robustness when applying recursive identification where the U.S. factors are

ordered above the oil factors in the FAVAR. That is, we assume that the U.S. factors

cannot react contemporaneously to the oil factors. The results are given in the figures in

Appendix E.4. The results are quite similar. There are only two exceptions. First, prices

seem to react somewhat weaker to both a oil-specific demand shock and a shock to global

oil production. Second, the effect on employment after a shock to global oil production

is smaller. As a consequence the response in the Fed funds rate is also smaller.

5.5 Alternative transformation

We use the same transformation of the oil variables in our FAVAR model as in Kil-

ian (2009). In particular, the real price of oil is in logs in our model. Several papers use

instead first difference of the logarithm of the real price of oil.21 We check for robustness

with respect to such a transformation to the real price of oil. The results are given in

Appendix E.5. Again, our results seem to be robust. The only differences are in the

response of financial variables and the response of consumer prices and employment mea-

21See for instance Blanchard and Gali (2007) and Boivin, Giannoni, and Mojon (2008).

24

sures after 3 - 4 years to a oil-specific demand shock and a shock to global oil production.

In addition, the response of the real price of oil to the different shocks is more persistent.

5.6 Smaller data set

In this paper, we extract factors from 652 U.S. macroeconomic variables and disaggre-

gated price and quantity series. We are interested in studying the effect of different type

of oil price shocks on the U.S. economy. Other authors, such as Stock and Watson (2002a)

and Bernanke, Boivin, and Eliasz (2005), have studied the effect of other shocks on the

U.S economy extracting factors from about a hundred macroeconomic series. As a ro-

bustness check, we re-estimate the FAVAR model extracting factors from only the 110

U.S. macroeconomic variables included in our original data set.22

The figures in Appendix E.6 show the results of extracting factors from the small

data set (110 data series) and the large data set (652 data series). The figures show

some differences. First, the negative effect on industrial production from a negative

shock to global oil production is weaker than in our preferred FAVAR model. The same

holds for employment and consumer prices. As a consequence of this, the effect on the

Fed funds rate is almost neutral. Second, the negative effect on industrial production

and employment from a oil-specific demand shock is much weaker than in our preferred

FAVAR model. The response of consumer prices is also larger and together this leads

to an increase in the Fed funds rate opposed to a decrease as indicated by our preferred

model. Third, the response of the financial variables are in general somewhat different.

However, these variables are not very well captured in any of the two models.

6 Conclusion

This paper studies the impact of different type of oil price shocks on the U.S. economy us-

ing a factor-augmented vector autoregression (FAVAR) approach. This framework allows

22Note that to the extent that the disaggregated price and quantity series are also driven by macroeco-

nomic fluctuations, using the larger data set should not ”tilt” the factors in one direction at the expense

of other dimensions in the economy. That is, as long as we have included at least as many factors as

their true number. See Boivin, Giannoni, and Mihov (2009) for details.

25

us to study the transmission of oil price shocks to a large amount of U.S. macroeconomic

variables as well as the interaction between these shocks and monetary policy.

We find that oil demand and oil supply shocks have a rather different effect on the

dynamics of the real price of oil as well as on the U.S. macro economy. In particular, we

find that positive oil-specific demand shocks increase prices and have a negative effect on

the real economy and the stock market. They also cause a monetary tightening. Further,

we find that positive shocks to the global demand in commodity markets have a large

and persistent positive effect on prices. This causes a monetary tightening on impact.

Similar to the oil-specific shocks, these shocks also have a negative effect on the U.S. real

economy. However, the dynamic is different as the negative effect is delayed. Finally,

oil supply shocks have a small effect on the U.S. macro economy and cause negligible

movements in the Fed funds rate. The results are robust to a series of alternative model

variations.

Our results show that there are important differences in both the response of macroe-

conomic variables as well as monetary policy to the different type of oil price shocks. In

particular, oil demand shocks seem to be an important source to macroeconomic fluctua-

tions, while oil supply shocks seem to be far less important. This confirms the results and

conjectures in Kilian (2009). Further, the causes behind the change in oil demand seem

to be important. The U.S. economy responds differently to a shock to global demand for

industrial commodities than to a demand shock that is specific to the global crude oil

market. This indicates that the cause behind the movements in the oil price is important.

Hence, it suggests that monetary policy should respond differently to movements in the

real price of oil, depending on what causes the movements.

26

References

Bachmeier, L. (2008): “Monetary policy and the transmission of oil shocks,” Journal

of Macroeconomics, 30(4):1738–1755.

Bai, J. and S. Ng (2002): “Determining the Number of Factors in Approximate

Factor Models,” Econometrica, 70(1):191–221.

Barsky, R. B. and L. Kilian (2002): “Do We Really Know that Oil Caused the

Great Stagflation? A Monetary Alternative,” in Bernanke, B. S. and K. Rogoff, editors,

NBER Macroeconomics Annual 2001, pages 137–183. MIT Press: Cambridge, MA.

(2004): “Oil and the Macroeconomy since the 1970s,” Journal of Economic

Perspectives, 18(4):115–134.

Baumeister, C. and G. Peersman (2008): “Time-Varying Effects of Oil Supply

Shocks on the US Economy,” Working Papers of Faculty of Economics and Business

Administration, Ghent University, Belgium, Ghent University, Faculty of Economics

and Business Administration.

Bernanke, B., J. Boivin, and P. S. Eliasz (2005): “Measuring the Effects of

Monetary Policy: A Factor-augmented Vector Autoregressive (FAVAR) Approach,” The

Quarterly Journal of Economics, 120(1):387–422.

Bernanke, B. S., M. Gertler, and M. Waston (1997): “Systematic Monetary

Policy and the Effects of Oil Price Shocks,” Brookings Papers on Economic Activity,

28(1):91–142.

Bernanke, B. S., M. Gertler, and M. W. Watson (2004): “Oil Shocks and

Aggregate Macroeconomic Behavior: The Role of Monetary Policy: Reply,” Journal of

Money, Credit and Banking, 36(2):287–291.

Bernanke, B. S. and I. Mihov (1998): “Measuring Monetary Policy,” The Quarterly

Journal of Economics, 113(3):869–902.

27

Bjørnland, H. C. (2000): “The Dynamic Effects of Aggregate Demand, Supply and

Oil Price Shocks–A Comparative Study,” The Manchester School of Economic Studies,

68(5):578–607.

(2009): “Oil Price Shocks and Stock Market Booms in an Oil Exporting

Country,” Scottish Journal of Political Economy, Forthcoming.

Blanchard, O. J. and J. Gali (2007): “The Macroeconomic Effects of Oil Shocks:

Why are the 2000s So Different from the 1970s?,” NBER Working Papers 13368, Na-

tional Bureau of Economic Research, Inc.

Boivin, J. and M. Giannoni (2008): “Global Forces and Monetary Policy Effective-

ness,” NBER Working Papers 13736, National Bureau of Economic Research, Inc.

Boivin, J., M. Giannoni, and I. Mihov (2009): “Sticky Prices and Monetary Policy:

Evidence from Disaggregated U.S. Data,” American Economic Review, Forthcoming.

Boivin, J. and M. P. Giannoni (2006): “Has Monetary Policy Become More Effec-

tive?,” The Review of Economics and Statistics, 88(3):445–462.

Boivin, J., M. P. Giannoni, and B. Mojon (2008): “How Has the Euro Changed

the Monetary Transmission?,” NBER Macroeconomics Annual, Forthcoming.

Boivin, J. and S. Ng (2006): “Are more data always better for factor analysis?,”

Journal of Econometrics, 132(1):169–194.

Chamberlain, G. and M. Rothschild (1983): “Arbitrage, Factor Structure, and

Mean-Variance Analysis on Large Asset Markets,” Econometrica, 51(5):1281–304.

Clarida, R., J. Gali, and M. Gertler (2000): “Monetary Policy Rules And

Macroeconomic Stability: Evidence And Some Theory,” The Quarterly Journal of Eco-

nomics, 115(1):147–180.

Forni, M., M. Hallin, M. Lippi, and L. Reichlin (2000): “The Generalized

Dynamic-Factor Model: Identification And Estimation,” The Review of Economics and

Statistics, 82(4):540–554.

28

Granger, C. W. J. (1969): “Investigating Causal Relations by Econometric Models

and Cross-Spectral Methods,” Econometrica, 37(3):424–38.

Hamilton, J. D. (1983): “Oil and the Macroeconomy since World War II,” Journal

of Political Economy, 91(2):228–48.

(1996): “This is what happened to the oil price-macroeconomy relation-

ship,” Journal of Monetary Economics, 38(2):215–220.

(2003): “What is an oil shock?,” Journal of Econometrics, 113(2):363–398.

Hamilton, J. D. and A. M. Herrera (2004): “Oil Shocks and Aggregate Macroe-

conomic Behavior: The Role of Monetary Policy: Comment,” Journal of Money, Credit

and Banking, 36(2):265–86.

Herrera, A. M. and E. Pesavento (2009): “Oil price shocks, systematic monetary

policy and the great moderation,” Macroeconomic Dynamics, Forthcoming.

Jimenez-Rodriguez, R. and M. Sanchez (2005): “Oil price shocks and real GDP

growth: empirical evidence for some OECD countries,” Applied Economics, 37(2):201–

228.

Jones, C. M. and G. Kaul (1996): “Oil and the Stock Markets,” The Journal of

Finance, 51(2):463–491.

Kilian, L. (1998): “Small-Sample Confidence Intervals For Impulse Response Func-

tions,” The Review of Economics and Statistics, 80(2):218–230.

(2008a): “A Comparison of the Effects of Exogenous Oil Supply Shocks

on Output and Inflation in the G7 Countries,” Journal of the European Economic

Association, 6(1):78–121.

(2008b): “Exogenous Oil Supply Shocks: How Big Are They and How Much

Do They Matter for the U.S. Economy?,” The Review of Economics and Statistics,

90(2):216–240.

29

(2009): “Not All Oil Price Shocks Are Alike: Disentangling Demand and

Supply Shocks in the Crude Oil Market,” American Economic Review, Forthcoming.

Kilian, L. and C. Park (2007): “The Impact of Oil Price Shocks on the U.S. Stock

Market,” CEPR Discussion Papers, C.E.P.R. Discussion Papers.

Mork, K. A. (1989): “Oil and Macroeconomy When Prices Go Up and Down: An

Extension of Hamilton’s Results,” Journal of Political Economy, 97(3):740–44.

Nandha, M. and R. Faff (2008): “Does oil move equity prices? A global view,”

Energy Economics, 30(3):986 – 997.

Primiceri, G. E. (2005): “Time Varying Structural Vector Autoregressions and Mon-

etary Policy,” Review of Economic Studies, 72(3):821–852.

Sadorsky, P. (1999): “Oil Price Shocks and Stock Markets Activity,” Energy Eco-

nomics, 21(5):449–469.

Stock, J. H. and M. W. Watson (1996): “Evidence on Structural Instability in

Macroeconomic Time Series Relations,” Journal of Business & Economic Statistics,

14(1):11–30.

(2002a): “Forecasting Using Principal Components From a Large Number

of Predictors,” Journal of the American Statistical Association, 97:1167–1179.

(2002b): “Macroeconomic Forecasting Using Diffusion Indexes,” Journal

of Business & Economic Statistics, 20(2):147–62.

7 Appendix

A Variance Decomposition

30

Oil ProductionHorizon/Shocks Interest rate Oil production Global demand Oil price F 1 F 2 F 3 F 4 F 51 0 92 0 1 0 2 1 1 36 0 78 3 4 2 3 4 2 312 2 67 7 4 2 3 5 6 424 2 62 8 5 3 4 7 5 548 3 60 8 5 3 4 7 5 5120 3 60 8 5 3 4 7 5 5Global demandHorizon/Shocks Interest rate Oil production Global demand Oil price F 1 F 2 F 3 F 4 F 51 0 0 98 1 0 0 0 0 06 0 1 87 4 1 3 2 1 112 1 1 73 8 1 4 7 2 324 1 1 59 9 1 3 6 3 1848 2 1 46 7 1 3 19 2 19120 2 1 43 7 1 5 21 2 17Oil PriceHorizon/Shocks Interest rate Oil production Global demand Oil price F 1 F 2 F 3 F 4 F 51 0 2 1 94 0 0 1 0 06 0 10 4 75 2 2 5 1 112 1 8 9 59 3 1 9 1 924 1 6 14 43 3 1 11 2 2048 2 4 14 37 3 2 8 3 28120 3 4 11 35 4 3 9 2 30Factor 1Horizon/Shocks Interest rate Oil production Global demand Oil price F 1 F 2 F 3 F 4 F 51 10 0 1 2 80 5 1 0 06 14 1 3 2 65 7 1 4 412 10 2 7 2 48 18 3 4 624 7 4 5 4 30 33 6 3 748 7 4 13 3 16 40 7 2 8120 6 3 15 8 12 35 7 2 11Factor 2Horizon/Shocks Interest rate Oil production Global demand Oil price F 1 F 2 F 3 F 4 F 51 2 2 1 15 1 73 4 1 16 11 3 8 9 7 48 9 3 212 17 2 6 7 6 39 11 8 524 13 2 9 6 6 29 14 9 1248 10 3 10 13 6 23 18 7 10120 8 4 14 13 6 24 17 6 9Factor 2Horizon/Shocks Interest rate Oil production Global demand Oil price F 1 F 2 F 3 F 4 F 51 1 2 0 5 4 6 74 5 26 3 2 1 9 5 7 62 7 412 3 5 3 10 5 9 53 7 624 3 5 5 11 5 9 48 7 748 3 5 7 10 4 9 47 7 8120 4 5 8 9 5 10 45 6 8Factor 4Horizon/Shocks Interest rate Oil production Global demand Oil price F 1 F 2 F 3 F 4 F 51 5 2 0 1 10 6 1 73 16 6 4 2 10 8 7 3 59 112 6 5 4 12 9 10 4 49 224 5 6 5 17 7 11 5 41 248 4 5 14 16 6 11 5 34 4120 4 5 15 15 6 11 8 30 6Factor 5Horizon/Shocks Interest rate Oil production Global demand Oil price F 1 F 2 F 3 F 4 F 51 1 3 1 1 2 24 8 7 546 3 4 2 2 5 22 10 7 4612 3 4 5 6 6 19 9 8 3924 4 4 7 7 6 19 9 9 3648 4 4 8 7 6 19 10 9 35120 4 4 9 6 6 19 10 8 33

31

Interest RateHorizon/Shocks Interest rate Oil production Global demand Oil price F 1 F 2 F 3 F 4 F 51 78 0 1 0 14 0 4 0 26 40 0 6 0 13 2 24 11 312 22 1 10 1 9 13 29 9 524 13 2 8 5 6 22 34 5 448 10 3 10 5 5 35 23 4 4120 9 2 12 8 4 34 17 4 10Consumer PricesHorizon/Shocks Interest rate Oil production Global demand Oil price F 1 F 2 F 3 F 4 F 51 3 2 0 27 23 9 6 24 56 3 2 4 24 18 11 6 24 812 4 3 8 17 15 20 5 17 1024 5 4 6 13 11 26 6 12 1648 6 3 14 10 8 26 10 9 15120 5 3 15 14 9 22 10 7 15Industrial ProductionHorizon/Shocks Interest rate Oil production Global demand Oil price F 1 F 2 F 3 F 4 F 51 0 2 1 1 6 13 61 5 106 5 2 2 6 9 14 48 7 712 6 3 4 6 7 14 43 10 724 5 4 9 6 7 13 39 10 748 5 4 11 8 6 14 36 9 6120 4 4 14 8 6 14 35 8 6Producer PricesHorizon/Shocks Interest rate Oil production Global demand Oil price F 1 F 2 F 3 F 4 F 51 1 1 0 36 6 10 7 23 156 1 3 3 34 5 10 6 21 1612 3 3 7 27 6 14 5 16 1824 4 3 6 21 6 18 6 13 2448 5 3 11 18 6 17 9 10 22120 4 4 13 20 8 15 9 8 19Unemployment rateHorizon/Shocks Interest rate Oil production Global demand Oil price F 1 F 2 F 3 F 4 F 51 5 2 5 0 1 3 12 57 146 3 5 10 22 2 4 7 40 812 3 8 10 23 4 6 6 32 724 3 8 12 27 3 6 6 27 748 2 5 23 22 3 5 8 20 12120 4 4 21 17 3 9 12 14 16Stock PricesHorizon/Shocks Interest rate Oil production Global demand Oil price F 1 F 2 F 3 F 4 F 51 1 1 1 25 0 4 25 39 46 2 3 2 25 3 6 23 33 412 4 4 3 23 4 10 21 26 524 4 4 3 21 6 11 21 23 748 4 5 3 22 7 10 20 22 7120 3 5 6 22 7 11 19 19 7Dividend YieldsHorizon/Shocks Interest rate Oil production Global demand Oil price F 1 F 2 F 3 F 4 F 51 4 0 1 7 17 1 59 4 66 6 1 2 12 15 3 50 5 612 7 3 2 13 15 5 42 5 724 7 4 3 13 15 6 40 5 748 6 4 4 14 14 6 38 5 8120 6 4 4 14 14 7 37 5 8Commodity Price IndexHorizon/Shocks Interest rate Oil production Global demand Oil price F 1 F 2 F 3 F 4 F 51 1 4 9 14 9 39 10 4 96 6 3 28 8 7 24 15 3 612 12 2 24 6 5 19 18 7 724 10 2 19 5 4 14 18 9 2048 8 2 17 9 4 10 26 7 18120 7 2 17 11 4 11 24 6 17

32

Exchange Rate (Yen)Horizon/Shocks Interest rate Oil production Global demand Oil price F 1 F 2 F 3 F 4 F 51 22 1 29 16 17 5 5 2 36 13 3 33 13 12 3 12 4 612 10 3 33 11 10 2 18 3 924 9 3 32 12 9 2 22 3 848 7 4 30 14 9 5 21 2 7120 7 4 31 14 9 5 21 2 7EmploymentHorizon/Shocks Interest rate Oil production Global demand Oil price F 1 F 2 F 3 F 4 F 51 2 4 1 0 3 45 33 2 116 5 4 1 8 6 34 33 4 612 8 4 2 9 4 31 30 6 524 7 4 12 10 4 26 26 7 548 6 4 14 11 4 23 26 7 5120 6 4 17 10 3 21 26 6 6New OrdersHorizon/Shocks Interest rate Oil production Global demand Oil price F 1 F 2 F 3 F 4 F 51 4 1 1 0 0 25 29 0 396 10 2 4 4 8 19 29 4 1912 12 2 4 4 7 16 27 11 1624 10 3 11 4 6 15 26 12 1348 7 4 13 9 5 19 24 9 10120 7 4 18 8 5 19 23 8 9Housing StartsHorizon/Shocks Interest rate Oil production Global demand Oil price F 1 F 2 F 3 F 4 F 51 1 1 0 25 1 30 16 25 16 8 2 2 17 13 22 19 16 112 11 2 2 14 13 21 19 15 424 9 3 6 13 14 18 18 13 748 6 5 8 19 13 19 16 9 5120 5 4 16 16 11 21 15 7 5Consumer ExpectationsHorizon/Shocks Interest rate Oil production Global demand Oil price F 1 F 2 F 3 F 4 F 51 7 0 1 1 29 4 31 21 66 4 1 1 9 20 5 35 16 812 4 1 3 11 18 6 33 12 1024 4 2 9 11 17 8 27 10 1348 4 3 11 8 12 14 26 7 15120 4 3 17 8 11 14 25 6 13PCE PricesHorizon/Shocks Interest rate Oil production Global demand Oil price F 1 F 2 F 3 F 4 F 51 3 0 0 26 41 11 2 11 46 3 1 2 25 33 12 2 11 1112 4 2 5 19 26 19 2 8 1524 5 2 4 14 18 25 3 6 2248 6 2 10 11 13 25 8 4 21120 5 3 11 16 13 20 8 4 21PCE ConsumptionHorizon/Shocks Interest rate Oil production Global demand Oil price F 1 F 2 F 3 F 4 F 51 0 1 0 1 0 22 45 10 216 4 2 1 1 3 20 39 11 1912 5 4 2 5 4 21 33 10 1624 5 4 2 7 4 21 31 9 1648 5 5 5 7 4 21 30 9 15120 5 5 6 7 4 21 30 9 15

Table A-1: Variance decomposition for all variables in Ct and for selected variables ofinterest in Xt.

33

B Model statistics

Variables U.S. Factors Oil Factors All Factors

All U.S. data Xt (average) 0.21 0.07 0.23

US Macro data (110 series) (average) 0.46 0.13 0.49

Oil Production 0.07 1.00∗ 1.00∗

Global Demand 0.34 1.00∗ 1.00∗

Oil Price 0.71 1.00∗ 1.00∗

Interest rate 1.00∗ 0.45 1.00∗

Industrial Production 0.61 0.04 0.62

Consumer Price Index 0.77 0.34 0.80

Unemployment rate 0.72 0.52 0.77

S&P Stock Price 0.09 0.01 0.12

S&P Dividens yields 0.08 0.01 0.09

NAPM Commodity Price Index 0.65 0.27 0.70

Exchange rate (Yen) 0.03 0.02 0.05

Employment 0.56 0.04 0.59

New Orders 0.71 0.04 0.72

Housing Starts 0.52 0.01 0.59

Consumer Expenditure 0.61 0.28 0.62

Producer Price Index 0.49 0.20 0.54

PCE Price (all items) 0.76 0.40 0.79

PCE Consumption (all items) 0.49 0.01 0.50

Table A-2: Shows the fraction of the variance explained by the common factors for selected

variables in equation (3) (R2). U.S. Factors denotes regression including only F and Fed

funds rate as common factors in Ct. Oil Factors denotes regression including only the

three oil variables as common factors in Ct. All factors denotes regression including all

the common factors in Ct.∗ This is by construction

34

Variables P-value

Factor 1 0.000

Factor 2 0.000

Factor 3 0.000

Factor 4 0.000

Factor 5 0.000

Interest rate 0.000

Table A-3: Granger-causality test for oil factors affecting U.S. factors. Table reports

p-values

Oil prod Global demand Oil price F 1 F 2 F 3 F 4 F 5 Interest rate

0.71 0.99 0.99 0.98 0.94 0.81 0.86 0.78 1.00

Table A-4: Correlation between actual and fitted values for the reduced form VAR in

equation (1)

C Description of data set

We apply the following transformations to the raw data in order to induce stationarity: 1

= No transformation, 2 = First differences, 4 = Logarithm, 5 = First differences in logs.

Below is a complete description of the data set.

35

Description TransformationOUT ----------- real output and income

1 INDUSTRIAL PRODUCTION INDEX - PRODUCTS, TOTAL 52 INDUSTRIAL PRODUCTION INDEX - FINAL PRODUCTS 53 INDUSTRIAL PRODUCTION INDEX - CONSUMER GOODS 54 INDUSTRIAL PRODUCTION INDEX - DURABLE CONSUMER GOODS 55 INDUSTRIAL PRODUCTION INDEX - NONDURABLE CONSUMER GOODS 56 INDUSTRIAL PRODUCTION INDEX - BUSINESS EQUIPMENT 57 INDUSTRIAL PRODUCTION INDEX - MATERIALS 58 INDUSTRIAL PRODUCTION INDEX - DURABLE GOODS MATERIALS 59 INDUSTRIAL PRODUCTION INDEX - NONDURABLE GOODS MATERIALS 5

10 INDUSTRIAL PRODUCTION INDEX - MANUFACTURING (SIC) 511 INDUSTRIAL PRODUCTION INDEX - MINING NAICS=21 512 INDUSTRIAL PRODUCTION INDEX - ELECTRIC AND GAS UTILITIES 513 INDUSTRIAL PRODUCTION INDEX - TOTAL INDEX 514 PURCHASING MANAGERS' INDEX (SA) 515 NAPM PRODUCTION INDEX (PERCENT) 516 PERSONAL INCOME (CHAINED) (BIL2000$,SAAR) 517 PERSONAL INCOME LESS TRANSFER PAYMENTS (CHAINED) (BIL 2000$,SAAR) 518 INDUSTRIAL PRODUCTION INDEX - RESIDENTIAL UTILITIES 519 INDUSTRIAL PRODUCTION INDEX - BASIC METALS 5

EMP ------------- employment and hours20 INDEX OF HELP-WANTED ADVERTISING IN NEWSPAPERS (1967=100;SA) 521 EMPLOYMENT: RATIO; HELP-WANTED ADS:NO. UNEMPLOYED CLF 422 CIVILIAN LABOR FORCE: EMPLOYED, TOTAL (THOUS.,SA) 523 CIVILIAN LABOR FORCE: EMPLOYED, NONAGRIC.INDUSTRIES (THOUS.,SA) 524 UNEMPLOYMENT RATE: ALL WORKERS, 16 YEARS & OVER (%,SA) 125 UNEMPLOY.BY DURATION: AVERAGE(MEAN)DURATION IN WEEKS (SA) 126 UNEMPLOY.BY DURATION: PERSONS UNEMPL.LESS THAN 5 WKS (THOUS.,SA) 127 UNEMPLOY.BY DURATION: PERSONS UNEMPL.5 TO 14 WKS (THOUS.,SA) 128 UNEMPLOY.BY DURATION: PERSONS UNEMPL.15 WKS + (THOUS.,SA) 129 UNEMPLOY.BY DURATION: PERSONS UNEMPL.15 TO 26 WKS (THOUS.,SA) 130 Total Nonfarm Employment - Seasonally Adjusted - CES0000000001 531 Total Private Employment - Seasonally Adjusted - CES0500000001 532 Goods-producing Employment - Seasonally Adjusted - CES0600000001 533 Natural Resources and Mining Employment - Seasonally Adjusted - CES1000000001 534 Construction Employment - Seasonally Adjusted - CES2000000001 535 Manufacturing Employment - Seasonally Adjusted - CES3000000001 536 Durable Goods Manufacturing Employment - Seasonally Adjusted - CES3100000001 537 Nondurable Goods Manufacturing Employment - Seasonally Adjusted - CES3200000001 538 Service-providing Employment - Seasonally Adjusted - CES0700000001 539 Trade, Transportation, and Utilities Employment - Seasonally Adjusted - CES4000000001 540 Retail Trade Employment - Seasonally Adjusted - CES4200000001 541 Wholesale Trade Employment - Seasonally Adjusted - CES4142000001 542 Financial Activities Employment - Seasonally Adjusted - CES5500000001 543 Private Service-providing Employment - Seasonally Adjusted - CES0800000001 544 Government Employment - Seasonally Adjusted - CES9000000001 545 Manufacturing Average Weekly Hours of Production Workers - Seasonally Adjusted - CES3000000005 146 Manufacturing Average Weekly Overtime of Production Workers - Seasonally Adjusted - CES3000000007 147 NAPM EMPLOYMENT INDEX (PERCENT)

HSS -------------- housing starts and sales48 HOUSING STARTS:NONFARM(1947-58);TOTAL FARM&NONFARM(1959-)(THOUS.,SA 449 HOUSING STARTS:NORTHEAST (THOUS.U.)S.A. 450 HOUSING STARTS:MIDWEST(THOUS.U.)S.A. 451 HOUSING STARTS:SOUTH (THOUS.U.)S.A. 452 HOUSING STARTS:WEST (THOUS.U.)S.A. 453 HOUSING AUTHORIZED: TOTAL NEW PRIV HOUSING UNITS (THOUS.,SAAR) 454 MOBILE HOMES: MANUFACTURERS' SHIPMENTS (THOUS.OF UNITS,SAAR) 4

INV ---------------- real inventories and inventory-sales ratios55 NAPM INVENTORIES INDEX (PERCENT) 1

ORD--------------- orders and unfilled orders56 NAPM NEW ORDERS INDEX (PERCENT) 157 NAPM VENDOR DELIVERIES INDEX (PERCENT) 158 NEW ORDERS (NET) - CONSUMER GOODS & MATERIALS, 1996 DOLLARS (BCI) 559 NEW ORDERS, NONDEFENSE CAPITAL GOODS, IN 1996 DOLLARS (BCI) 5

SPR --------------- stock prices60 S&P'S COMMON STOCK PRICE INDEX: COMPOSITE (1941-43=10) 561 S&P'S COMMON STOCK PRICE INDEX: INDUSTRIALS (1941-43=10) 5

36

62 S&P'S COMPOSITE COMMON STOCK: DIVIDEND YIELD (% PER ANNUM) 163 S&P'S COMPOSITE COMMON STOCK: PRICE-EARNINGS RATIO (%,NSA) 164 COMMON STOCK PRICES: DOW JONES INDUSTRIAL AVERAGE

EXR ---------------- exchange rates65 FOREIGN EXCHANGE RATE: SWITZERLAND (SWISS FRANC PER U.S.$) 566 FOREIGN EXCHANGE RATE: JAPAN (YEN PER U.S.$) 567 FOREIGN EXCHANGE RATE: UNITED KINGDOM (CENTS PER POUND) 568 FOREIGN EXCHANGE RATE: CANADA (CANADIAN $ PER U.S.$) 5

INT ---------------- interest rates69 INTEREST RATE: FEDERAL FUNDS (EFFECTIVE) (% PER ANNUM,NSA) 170 INTEREST RATE: U.S.TREASURY BILLS,SEC MKT,3-MO.(% PER ANN,NSA) 171 INTEREST RATE: U.S.TREASURY BILLS,SEC MKT,6-MO.(% PER ANN,NSA) 172 INTEREST RATE: U.S.TREASURY CONST MATURITIES,1-YR.(% PER ANN,NSA) 173 INTEREST RATE: U.S.TREASURY CONST MATURITIES,5-YR.(% PER ANN,NSA) 174 INTEREST RATE: U.S.TREASURY CONST MATURITIES,10-YR.(% PER ANN,NSA) 175 BOND YIELD: MOODY'S AAA CORPORATE (% PER ANNUM) 176 BOND YIELD: MOODY'S BAA CORPORATE (% PER ANNUM) 177 Spread FYGM3 - FYFF 178 Spread FYGM6 - FYFF 179 Spread FYGT1 - FYFF 180 Spread FYGT5 - FYFF 181 Spread FYGT10 - FYFF 182 Spread FYAAAC - FYFF 183 Spread FYBAAC - FYFF 1

MON ---------------- money and credit quantity aggregates84 MONEY STOCK: M1(CURR,TRAV.CKS,DEM DEP,OTHER CK'ABLE DEP)(BIL$,SA) 585 MONEY STOCK:M2(M1+O'NITE RPS,EURO$,G/P&B/D MMMFS&SAV&SM TIME DEP(BIL$, 586 MONEY STOCK: M3(M2+LG TIME DEP,TERM RP'S&INST ONLY MMMFS)(BIL$,SA) 587 MONEY SUPPLY - M2 IN 1996 DOLLARS (BCI) 588 MONETARY BASE, ADJ FOR RESERVE REQUIREMENT CHANGES(MIL$,SA) 589 DEPOSITORY INST RESERVES:TOTAL,ADJ FOR RESERVE REQ CHGS(MIL$,SA) 590 DEPOSITORY INST RESERVES:NONBORROWED,ADJ RES REQ CHGS(MIL$,SA) 591 WKLY RP LG COM'L BANKS:NET CHANGE COM'L & INDUS LOANS(BIL$,SAAR) 192 CONSUMER CREDIT OUTSTANDING - NONREVOLVING(G19) 593 COMMERCIAL & INDUSTRIAL LOANS OUSTANDING IN 1996 DOLLARS 5

PRI --------------- price indexes94 NAPM COMMODITY PRICES INDEX (PERCENT) 195 PRODUCER PRICE INDEX: FINISHED GOODS (82=100,SA) 596 PRODUCER PRICE INDEX:FINISHED CONSUMER GOODS (82=100,SA) 597 PRODUCER PRICE INDEX:INTERMED MAT.SUPPLIES & COMPONENTS(82=100,SA) 598 PRODUCER PRICE INDEX:CRUDE MATERIALS (82=100,SA) 599 CPI-U: ALL ITEMS (82-84=100,SA) 5

100 CPI-U: APPAREL & UPKEEP (82-84=100,SA) 5101 CPI-U: TRANSPORTATION (82-84=100,SA) 5102 CPI-U: MEDICAL CARE (82-84=100,SA) 5103 CPI-U: COMMODITIES (82-84=100,SA) 5104 CPI-U: DURABLES (82-84=100,SA) 5105 CPI-U: ALL ITEMS LESS FOOD (82-84=100,SA) 5106 CPI-U: ALL ITEMS LESS SHELTER (82-84=100,SA) 5107 CPI-U: ALL ITEMS LESS MIDICAL CARE (82-84=100,SA) 5108 SPOT MARKET PRICE INDEX:BLS & CRB: ALL COMMODITIES(1967=100) 5

AHE ------------- average hourly earnings109 Construction Average Hourly Earnings of Production Workers - Seasonally Adjusted - CES2000000006 5110 Manufacturing Average Hourly Earnings of Production Workers - Seasonally Adjusted - CES3000000006 5

111 U. OF MICH. INDEX OF CONSUMER EXPECTATIONS(BCD-83) 1

Oil market112 U.S. CRUDE OIL IMPORTED ACQUISITION COST BY REFINERS 4113 CRUDE OIL PRODUCTION, WORLD 5114 INDEX OF GLOBAL REAL ECONOMIC ACTIVITY IN INDUSTRIAL COMMODITY MARKETS 1

37

Personal Consumption Data, Prices and QuantitiesName Sample Transformation Description

1 P1NDCG3 1976:2 - 2005:6 5 New domestic autos

2 P1NFCG3 1976:2 - 2005:6 5 New foreign autos

3 P1NETG3 1976:2 - 2005:6 5 Net transactions in used autos

4 P1MARG3 1976:2 - 2005:6 5 Used auto margin

5 P1REEG3 1976:2 - 2005:6 5 Employee reimbursement

6 P1TRUG3 1976:2 - 2005:6 5 Trucks, new and net used

7 P1REVG3 1976:2 - 2005:6 5 Recreational vehicles

8 P1TATG3 1976:2 - 2005:6 5 Tires and tubes

9 P1PAAG3 1976:2 - 2005:6 5 Accessories and parts

10 P1FNRG3 1976:2 - 2005:6 5 Furniture, including mattresses and bedsprings

11 P1MHAG3 1976:2 - 2005:6 5 Major household appliances

12 P1SEAG3 1976:2 - 2005:6 5 Small electric appliances

13 P1CHNG3 1976:2 - 2005:6 5 China, glassware, tableware, and utensils

14 P1RADG3 1976:2 - 2005:6 5 Video and audio goods, including musical instruments, and computer goods

15 P1FLRG3 1976:2 - 2005:6 5 Floor coverings

16 P1CLFG3 1976:2 - 2005:6 5 Clocks, lamps, and furnishings

17 P1TEXG3 1976:2 - 2005:6 5 Blinds, rods, and other

18 P1WTRG3 1976:2 - 2005:6 5 Writing equipment

19 P1HDWG3 1976:2 - 2005:6 5 Tools, hardware, and supplies

20 P1LWNG3 1976:2 - 2005:6 5 Outdoor eqpt and supplies

21 P1OPTG3 1976:2 - 2005:6 5 Ophthalmic products and orthopedic appliances

22 P1GUNG3 1976:2 - 2005:6 5 Guns

23 P1SPTG3 1976:2 - 2005:6 5 Sporting equipment

24 P1CAMG3 1976:2 - 2005:6 5 Photographic equipment

25 P1BCYG3 1976:2 - 2005:6 5 Bicycles

26 P1MCYG3 1976:2 - 2005:6 5 Motorcycles

27 P1BOAG3 1976:2 - 2005:6 5 Pleasure boats

28 P1AIRG3 1976:2 - 2005:6 5 Pleasure aircraft

29 P1JRYG3 1976:2 - 2005:6 5 Jewelry and watches

30 P1BKSG3 1976:2 - 2005:6 5 Books and maps

31 P1GRAG3 1976:2 - 2005:6 5 Cereals

32 P1BAKG3 1976:2 - 2005:6 5 Bakery products

33 P1BEEG3 1976:2 - 2005:6 5 Beef and veal

34 P1PORG3 1976:2 - 2005:6 5 Pork

35 P1MEAG3 1976:2 - 2005:6 5 Other meats

36 P1POUG3 1976:2 - 2005:6 5 Poultry

37 P1FISG3 1976:2 - 2005:6 5 Fish and seafood

38 P1GGSG3 1976:2 - 2005:6 5 Eggs

39 P1MILG3 1976:2 - 2005:6 5 Fresh milk and cream

40 P1DAIG3 1976:2 - 2005:6 5 Processed dairy products

41 P1FRUG3 1976:2 - 2005:6 5 Fresh fruits

42 P1VEGG3 1976:2 - 2005:6 5 Fresh vegetables

43 P1PFVG3 1976:2 - 2005:6 5 Processed fruits and vegetables

44 P1JNBG3 1976:2 - 2005:6 5 Juices and nonalcoholic drinks

45 P1CTMG3 1976:2 - 2005:6 5 Coffee, tea and beverage materials

46 P1FATG3 1976:2 - 2005:6 5 Fats and oils

47 P1SWEG3 1976:2 - 2005:6 5 Sugar and sweets

48 P1OFDG3 1976:2 - 2005:6 5 Other foods

49 P1PEFG3 1976:2 - 2005:6 5 Pet food

50 P1MLTG3 1976:2 - 2005:6 5 Beer and ale, at home

51 P1WING3 1976:2 - 2005:6 5 Wine and brandy, at home

52 P1LIQG3 1976:2 - 2005:6 5 Distilled spirits, at home

53 P1ESLG3 1976:2 - 2005:6 5 Elementary and secondary school lunch

54 P1HSLG3 1976:2 - 2005:6 5 Higher education school lunch

55 P1OPMG3 1976:2 - 2005:6 5 Other purchased meals

56 P1APMG3 1976:2 - 2005:6 5 Alcohol in purchased meals

57 P1CFDG3 1976:2 - 2005:6 5 Food supplied civilians

58 P1MFDG3 1976:2 - 2005:6 5 Food supplied military

59 P1FFDG3 1976:2 - 2005:6 5 Food produced and consumed on farms

60 P1SHUG3 1976:2 - 2005:6 5 Shoes (12)

61 P1WGCG3 1976:2 - 2005:6 5 Clothing for females

62 P1WICG3 1976:2 - 2005:6 5 Clothing for infants

63 P1WSGG3 1976:2 - 2005:6 5 Sewing goods for females

64 P1WUGG3 1976:2 - 2005:6 5 Luggage for females

65 P1MBCG3 1976:2 - 2005:6 5 Clothing for males

66 P1MSGG3 1976:2 - 2005:6 5 Sewing goods for males

67 P1MUGG3 1976:2 - 2005:6 5 Luggage for males

68 P1MICG3 1976:2 - 2005:6 5 Standard clothing issued to military personnel (n.d.)

69 P1GASG3 1976:2 - 2005:6 5 Gasoline and other motor fuel

70 P1LUBG3 1976:2 - 2005:6 5 Lubricants

71 P1OILG3 1976:2 - 2005:6 5 Fuel oil

72 P1LPGG3 1976:2 - 2005:6 5 Liquified petroleum gas and other fuel

38

Name Sample Transformation Description73 P1TOBG3 1976:2 - 2005:6 5 Tobacco products

74 P1SOAG3 1976:2 - 2005:6 5 Soap

75 P1CSMG3 1976:2 - 2005:6 5 Cosmetics and perfumes

76 P1OPHG3 1976:2 - 2005:6 5 Other personal hygiene goods

77 P1SDHG3 1976:2 - 2005:6 5 Semidurable house furnishings

78 P1CLEG3 1976:2 - 2005:6 5 Cleaning preparations

79 P1LIGG3 1976:2 - 2005:6 5 Lighting supplies

80 P1PAPG3 1976:2 - 2005:6 5 Paper products

81 P1RXDG3 1976:2 - 2005:6 5 Prescription drugs

82 P1NRXG3 1976:2 - 2005:6 5 Nonprescription drugs

83 P1MDSG3 1976:2 - 2005:6 5 Medical supplies

84 P1GYNG3 1976:2 - 2005:6 5 Gynecological goods

85 P1DOLG3 1976:2 - 2005:6 5 Toys, dolls, and games

86 P1AMMG3 1976:2 - 2005:6 5 Sport supplies, including ammunition

87 P1FLMG3 1976:2 - 2005:6 5 Film and photo supplies

88 P1STSG3 1976:2 - 2005:6 5 Stationery and school supplies

89 P1GREG3 1976:2 - 2005:6 5 Greeting cards

90 P1ARTG3 1976:2 - 2005:6 5 Government expenditures abroad

91 P1ARSG3 1976:2 - 2005:6 5 Other private services

92 P1REMG3 1976:2 - 2005:6 5 Less: Personal remittances in kind to nonresidents

93 P1MGZG3 1976:2 - 2005:6 5 Magazines and sheet music

94 P1NWPG3 1976:2 - 2005:6 5 Newspapers

95 P1FLOG3 1976:2 - 2005:6 5 Flowers, seeds, and potted plants

96 P1OMHG3 1976:2 - 2005:6 5 Owner occupied mobile homes

97 P1OSTG3 1976:2 - 2005:6 5 Owner occupied stationary homes

98 P1TMHG3 1976:2 - 2005:6 5 Tenant occupied mobile homes

99 P1TSPG3 1976:2 - 2005:6 5 Tenant occupied stationary homes

100 P1TLDG3 1976:2 - 2005:6 5 Tenant landlord durables

101 P1FARG3 1976:2 - 2005:6 5 Rental value of farm dwellings

102 P1HOTG3 1976:2 - 2005:6 5 Hotels and motels

103 P1HFRG3 1976:2 - 2005:6 5 Clubs and fraternity housing

104 P1HHEG3 1976:2 - 2005:6 5 Higher education housing

105 P1HESG3 1976:2 - 2005:6 5 Elem and second education housing

106 P1TGRG3 1976:2 - 2005:6 5 Tenant group room and board

107 P1TGLG3 1976:2 - 2005:6 5 Tenant group employee lodging

108 P1ELCG3 1976:2 - 2005:6 5 Electricity

109 P1NGSG3 1976:2 - 2005:6 5 Gas

110 P1WSMG3 1976:2 - 2005:6 5 Water and sewerage maintenance

111 P1REFG3 1976:2 - 2005:6 5 Refuse collection

112 P1LOCG3 1976:2 - 2005:6 5 Local and cellular telephone

113 P1INCG3 1976:2 - 2005:6 5 Intrastate toll calls

114 P1ITCG3 1976:2 - 2005:6 5 Interstate toll calls

115 P1DMCG3 1976:2 - 2005:6 5 Domestic service, cash

116 P1DMIG3 1976:2 - 2005:6 5 Domestic service, in kind

117 P1MSEG3 1976:2 - 2005:6 5 Moving and storage

118 P1FIPG3 1976:2 - 2005:6 5 Household insurance premiums

119 P1FIBG3 1976:2 - 2005:6 5 Less: Household insurance benefits paid

120 P1RCLG3 1976:2 - 2005:6 5 Rug and furniture cleaning

121 P1EREG3 1976:2 - 2005:6 5 Electrical repair

122 P1FREG3 1976:2 - 2005:6 5 Reupholstery and furniture repair

123 P1PSTG3 1976:2 - 2005:6 5 Postage

124 P1MHOG3 1976:2 - 2005:6 5 Household operation services, n.e.c.

125 P1ARPG3 1976:2 - 2005:6 5 Motor vehicle repair

126 P1RLOG3 1976:2 - 2005:6 5 Motor vehicle rental, leasing, and other

127 P1TOLG3 1976:2 - 2005:6 5 Bridge, tunnel, ferry, and road tolls

128 P1AING3 1976:2 - 2005:6 5 Insurance

129 P1IMTG3 1976:2 - 2005:6 5 Mass transit systems

130 P1TAXG3 1976:2 - 2005:6 5 Taxicab

131 P1IRRG3 1976:2 - 2005:6 5 Railway

132 P1IBUG3 1976:2 - 2005:6 5 Bus

133 P1IAIG3 1976:2 - 2005:6 5 Airline

134 P1TROG3 1976:2 - 2005:6 5 Other

135 P1PHYG3 1976:2 - 2005:6 5 Physicians

136 P1DENG3 1976:2 - 2005:6 5 Dentists

137 P1OPSG3 1976:2 - 2005:6 5 Other professional services

138 P1NPHG3 1976:2 - 2005:6 5 Nonprofit

139 P1FPHG3 1976:2 - 2005:6 5 Proprietary

140 P1GVHG3 1976:2 - 2005:6 5 Government

141 P1NRSG3 1976:2 - 2005:6 5 Nursing homes

142 P1MING3 1976:2 - 2005:6 5 Medical care and hospitalization

143 P1IING3 1976:2 - 2005:6 5 Income loss

144 P1PWCG3 1976:2 - 2005:6 5 Workers' compensation

145 P1MOVG3 1976:2 - 2005:6 5 Motion picture theaters

39

Name Sample Transformation Description146 P1LEGG3 1976:2 - 2005:6 5 Legitimate theaters and opera, and entertainments of nonprofit institutions (except at

147 P1SPEG3 1976:2 - 2005:6 5 Spectator sports

148 P1RTVG3 1976:2 - 2005:6 5 Radio and television repair

149 P1CLUG3 1976:2 - 2005:6 5 Clubs and fraternal organizations

150 P1SIGG3 1976:2 - 2005:6 5 Sightseeing

151 P1FLYG3 1976:2 - 2005:6 5 Private flying

152 P1BILG3 1976:2 - 2005:6 5 Bowling and billiards

153 P1CASG3 1976:2 - 2005:6 5 Casino gambling

154 P1OPAG3 1976:2 - 2005:6 5 Other comml participant amusements

155 P1PARG3 1976:2 - 2005:6 5 Pari-mutuel net receipts

156 P1REOG3 1976:2 - 2005:6 5 Other

157 P1SCLG3 1976:2 - 2005:6 5 Shoe repair

158 P1DRYG3 1976:2 - 2005:6 5 Drycleaning

159 P1LGRG3 1976:2 - 2005:6 5 Laundry and garment repair

160 P1BEAG3 1976:2 - 2005:6 5 Beauty shops, including combination

161 P1BARG3 1976:2 - 2005:6 5 Barber shops

162 P1WCRG3 1976:2 - 2005:6 5 Watch, clock, and jewelry repair

163 P1CRPG3 1976:2 - 2005:6 5 Miscellaneous personal services

164 P1BROG3 1976:2 - 2005:6 5 Brokerage charges and investment counseling

165 P1BNKG3 1976:2 - 2005:6 5 Bank service charges, trust services, and safe deposit box rental

166 P1IMCG3 1976:2 - 2005:6 5 Commercial banks

167 P1IMNG3 1976:2 - 2005:6 5 Other financial institutions

168 P1LIFG3 1976:2 - 2005:6 5 Expense of handling life insurance and pension plans

169 P1GALG3 1976:2 - 2005:6 5 Legal services

170 P1FUNG3 1976:2 - 2005:6 5 Funeral and burial expenses

171 P1UNSG3 1976:2 - 2005:6 5 Labor union expenses

172 P1ASSG3 1976:2 - 2005:6 5 Profession association expenses

173 P1GENG3 1976:2 - 2005:6 5 Employment agency fees

174 P1AMOG3 1976:2 - 2005:6 5 Money orders

175 P1CLAG3 1976:2 - 2005:6 5 Classified ads

176 P1ACCG3 1976:2 - 2005:6 5 Tax return preparation services

177 P1THEG3 1976:2 - 2005:6 5 Personal business services, n.e.c.

178 P1PEDG3 1976:2 - 2005:6 5 Private higher education

179 P1GEDG3 1976:2 - 2005:6 5 Public higher education

180 P1ESCG3 1976:2 - 2005:6 5 Elementary and secondary schools

181 P1NSCG3 1976:2 - 2005:6 5 Nursery schools

182 P1VEDG3 1976:2 - 2005:6 5 Commercial and vocational schools

183 P1REDG3 1976:2 - 2005:6 5 Foundations and nonprofit research

184 P1POLG3 1976:2 - 2005:6 5 Political organizations

185 P1MUSG3 1976:2 - 2005:6 5 Museums and libraries

186 P1FOUG3 1976:2 - 2005:6 5 Foundations to religion and welfare

187 P1WELG3 1976:2 - 2005:6 5 Social welfare

188 P1RELG3 1976:2 - 2005:6 5 Religion

189 P1FTRG3 1976:2 - 2005:6 5 Foreign travel by U.S. residents

190 P1EXFG3 1976:2 - 2005:6 5 Less: Expenditures in the United States by nonresidents

191 P1TDGG3 1976:2 - 2005:6 5 Durable goods

192 P1TNDG3 1976:2 - 2005:6 5 Nondurable goods

193 P1TSSG3 1976:2 - 2005:6 5 Services

194 PPCE 1976:2 - 2005:6 5 Personal Consumption Expenditures (all items)

40

Producer Price Index DataName Sample Transformation Description

1 311119 1976:2 - 2005:6 5 Other animal food manufacturing2 311119p 1976:2 - 2005:6 5 Other animal food manufacturing (primary products)3 311211 1976:2 - 2005:6 5 Flour Milling4 311212 1976:2 - 2005:6 5 Rice milling5 311213 1976:2 - 2005:6 5 Malt mfg6 311223a 1976:2 - 2005:6 5 Other oilseed processing (Cottonseed cake and meal and other byproducts)7 311225p 1976:2 - 2005:6 5 Fats and oils refining and blending (Primary products)8 311311 1976:2 - 2005:6 5 Sugarcane mills9 311313 1976:2 - 2005:6 5 Beet sugar manufacturing10 311412 1976:2 - 2005:6 5 Frozen specialty food manufacturing11 311520 1976:2 - 2005:6 5 Ice cream and frozen dessert mfg12 311920 1976:2 - 2005:6 5 Coffee and tea manufacturing13 312140 1976:2 - 2005:6 5 Distilleries14 32211- 1976:2 - 2005:6 5 Pulp mills15 32213- 1976:2 - 2005:6 5 Paperboard mills16 325620p 1976:2 - 2005:6 5 Toilet preparation mfg (Primary products)17 325920 1976:2 - 2005:6 5 Explosives manufacturing18 32731- 1976:2 - 2005:6 5 Cement mfg19 327320 1976:2 - 2005:6 5 Ready mixed concrete mfg and dist20 327410 1976:2 - 2005:6 5 Lime21 327420 1976:2 - 2005:6 5 Gypsum building products manufacturing22 327910 1976:2 - 2005:6 5 Abrasive product manufacturing23 331210 1976:2 - 2005:6 5 Iron steel pipe & tube mfg from purch steel24 333210 1976:2 - 2005:6 5 Sawmill & woodworking machinery mfg25 334310 1976:2 - 2005:6 5 Audio & video equipment mfg26 335110 1976:2 - 2005:6 5 Electric lamp bulb & part mfg27 336370 1976:2 - 2005:6 5 Motor vehicle metal stamping28 337910 1976:2 - 2005:6 5 Mattress mfg29 311421 1976:2 - 2005:6 5 Fruit and vegetable canning30 311423 1976:2 - 2005:6 5 Dried and dehydrated food manufacturing31 311513 1976:2 - 2005:6 5 Cheese manufacturing32 311611 1976:2 - 2005:6 5 Animal except poultry slaughtering33 311612 1976:2 - 2005:6 5 Meat processed from carcasses34 311613 1976:2 - 2005:6 5 Rendering and meat byproduct processing35 311711 1976:2 - 2005:6 5 Seafood canning36 311712 1976:2 - 2005:6 5 Fresh & frozen seafood processing37 311813p 1976:2 - 2005:6 5 Frozen cakes pies & other pastries mfg (Primary products)38 3118233 1976:2 - 2005:6 5 Dry pasta manufacturing ( Macaroni spaghetti vermicelli and noodles)39 312111p 1976:2 - 2005:6 5 Soft drinks manufacturing (Primary products)40 312221 1976:2 - 2005:6 5 Cigarettes41 3122291 1976:2 - 2005:6 5 Other tobacco product mfg (Cigars)42 313111 1976:2 - 2005:6 5 Yarn spinning mills43 3133111 1976:2 - 2005:6 5 Broadwoven fabric finishing mills ( Finished cotton broadwoven fabrics not finished44 315111 1976:2 - 2005:6 5 Sheer hosiery mills45 315191 1976:2 - 2005:6 5 Outerwear knitting mills46 315223 1976:2 - 2005:6 5 Men's boy's cut & sew shirt exc work mfg47 315224 1976:2 - 2005:6 5 Men's boy's cut & sew trouser slack jean mfg48 315993 1976:2 - 2005:6 5 Men's and boys' neckwear mfg49 316211 1976:2 - 2005:6 5 Rubber and plastic footwear manufacturing50 316213 1976:2 - 2005:6 5 Men's footwear exc athletic mfg51 316214 1976:2 - 2005:6 5 Women's footwear exc athletic mfg52 316992 1976:2 - 2005:6 5 Women's handbag & purse mfg53 321212 1976:2 - 2005:6 5 Softwood veneer or plywood mfg54 3212191 1976:2 - 2005:6 5 Reconstituted wood product mfg ( Particleboard produced at this location)55 3219181 1976:2 - 2005:6 5 Other millwork including flooring ( Wood moldings except prefinished moldings ma56 321991 1976:2 - 2005:6 5 Manufactured homes mobile homes mfg57 3221211 1976:2 - 2005:6 5 Paper except newsprint mills ( Clay coated printing and converting paper)58 322214 1976:2 - 2005:6 5 Fiber can tube drum & oth products mfg59 324121 1976:2 - 2005:6 5 Asphalt paving mixture & block mfg60 324122 1976:2 - 2005:6 5 Asphalt shingle & coating materials mfg61 324191p 1976:2 - 2005:6 5 Petroleum lubricating oils and greases ( Primary products)62 325181 1976:2 - 2005:6 5 Alkalies and chlorine63 3251881 1976:2 - 2005:6 5 All other basic inorganic chemical manufacturing ( Sulfuric acid gross new and fort64 3251921 1976:2 - 2005:6 5 Cyclic crude and intermediate manufacturing ( Cyclic coal tar intermediates)65 325212 1976:2 - 2005:6 5 Synthetic rubber manufacturing66 325222 1976:2 - 2005:6 5 Manufactured noncellulosic fibers67 325314 1976:2 - 2005:6 5 Fertilizer mixing only manufacturing68 3254111 1976:2 - 2005:6 5 Medicinal & botanical mfg ( Synthetic organic medicinal chemicals in bulk)69 3261131 1976:2 - 2005:6 5 Unsupported plastics film sheet excluding packaging manufacturing ( Unsupported70 326192 1976:2 - 2005:6 5 Resilient floor covering manufacturing71 326211 1976:2 - 2005:6 5 Tire manufacturing except retreading72 327111 1976:2 - 2005:6 5 Vitreous plumbing fixtures access ftg mfg

41

Name Sample Transformation Description73 327121 1976:2 - 2005:6 5 Brick and structural clay tile74 327122 1976:2 - 2005:6 5 Ceramic wall and floor tile75 327124 1976:2 - 2005:6 5 Clay refractories76 327125 1976:2 - 2005:6 5 Nonclay Refractory Manufacturing77 327211 1976:2 - 2005:6 5 Flat glass manufacturing78 327213 1976:2 - 2005:6 5 Glass container manufacturing79 327331 1976:2 - 2005:6 5 Concrete block and brick manufacturing80 3279931 1976:2 - 2005:6 5 Mineral wool manufacturing81 331111 1976:2 - 2005:6 5 Iron and steel mills82 331112 1976:2 - 2005:6 5 Electrometallurgical ferroalloy product mfg83 331221 1976:2 - 2005:6 5 Rolled steel shape manufacturing84 331312 1976:2 - 2005:6 5 Primary aluminum production85 331315 1976:2 - 2005:6 5 Aluminum sheet plate & foil mfg86 331316 1976:2 - 2005:6 5 Aluminum extruded products87 331421 1976:2 - 2005:6 5 Copper rolling drawing & extruding88 3314913 1976:2 - 2005:6 5 Other nonferrous metal roll draw extruding ( Titanium and titanium base alloy mill sh89 3314923 1976:2 - 2005:6 5 Other nonferrous secondary smelt refine alloying (Secondary lead)90 331511 1976:2 - 2005:6 5 Iron foundries91 3322121 1976:2 - 2005:6 5 Hand and edge tools except machine tools and handsaws ( Mechanics' hand servic92 332213 1976:2 - 2005:6 5 Saw blade & handsaw mfg93 3323111 1976:2 - 2005:6 5 Prefabricated metal building and component manufacturing ( Prefabricated metal bu94 332321 1976:2 - 2005:6 5 Metal window and door manufacturing95 332431 1976:2 - 2005:6 5 Metal can mfg96 324393 1976:2 - 2005:6 5 Other metal container manufacturing ( Steel shipping barrels & drums exc beer ba97 332611 1976:2 - 2005:6 5 Spring heavy gauge mfg98 3326122 1976:2 - 2005:6 5 Spring light gauge mfg ( Precision mechanical springs)99 3327224 1976:2 - 2005:6 5 Bolt nut screw rivet & washer mfg ( Externally threaded metal fasteners except ai100 332913 1976:2 - 2005:6 5 Plumbing fixture fitting & trim mfg101 332991 1976:2 - 2005:6 5 Ball and roller bearings102 332992 1976:2 - 2005:6 5 Small arms ammunition mfg103 332996 1976:2 - 2005:6 5 Fabricated pipe & pipe fitting mfg104 332998 1976:2 - 2005:6 5 Enameled iron & metal sanitary ware mfg105 333111 1976:2 - 2005:6 5 Farm machinery & equipment mfg106 333131 1976:2 - 2005:6 5 Mining machinery & equipment mfg107 333132 1976:2 - 2005:6 5 Oil and gas field machinery and equipment mfg108 333292 1976:2 - 2005:6 5 Textile machinery109 333293 1976:2 - 2005:6 5 Printing machinery & equipment mfg110 3332941 1976:2 - 2005:6 5 Food products machinery mfg ( Dairy and milk products plant machinery)111 3332981 1976:2 - 2005:6 5 All other industrial machinery mfg ( Chemical manufacturing machinery equipment 112 3333111 1976:2 - 2005:6 5 Automatic vending machine mfg ( Automatic merchandising machines coin operate113 333512 1976:2 - 2005:6 5 Machine tool metal cutting types mfg114 333513 1976:2 - 2005:6 5 Machine tool metal forming types mfg115 3335151 1976:2 - 2005:6 5 Cutting tool & machine tool accessory mfg ( Small cutting tools for machine tools an116 333612 1976:2 - 2005:6 5 Speed changer industrial high speed drive & gear mfg117 333618 1976:2 - 2005:6 5 Other engine equipment mfg118 3339111 1976:2 - 2005:6 5 Pump & pumping equipment mfg ( Industrial pumps except hydraulic fluid power pu119 333922 1976:2 - 2005:6 5 Conveyor & conveying equipment mfg120 3339233 1976:2 - 2005:6 5 Overhead crane hoist & monorail system mfg ( Overhead traveling cranes and mon121 3339241 1976:2 - 2005:6 5 Industrial truck tractor trailer stacker machinery mfg ( Industrial trucks and tractors122 333992 1976:2 - 2005:6 5 Welding & soldering equipment mfg (Welding & soldering equipment mfg)123 333997 1976:2 - 2005:6 5 Scale & balance except laboratory mfg124 334411 1976:2 - 2005:6 5 Electron tube mfg125 334414 1976:2 - 2005:6 5 Electronic capacitor mfg126 334415 1976:2 - 2005:6 5 Electronic resistor mfg127 334417 1976:2 - 2005:6 5 Electronic connector mfg128 3345153 1976:2 - 2005:6 5 Electricity measuring testing instrument mfg ( Test equipment for testing electrical r129 334517p 1976:2 - 2005:6 5 Irradiation apparatus manufacturing ( Primary products)130 3351211 1976:2 - 2005:6 5 Residential electric lighting fixture mfg ( Residential electric lighting fixtures except 131 335122 1976:2 - 2005:6 5 Commercial electric lighting fixture mfg132 335129 1976:2 - 2005:6 5 Other lighting equipment mfg133 335212 1976:2 - 2005:6 5 Household vacuum cleaner mfg134 335221 1976:2 - 2005:6 5 Household cooking appliance mfg135 335311 1976:2 - 2005:6 5 Power distribution specialty transformer mfg136 335312 1976:2 - 2005:6 5 Motor & generator mfg137 335314p 1976:2 - 2005:6 5 Relay & industrial control mfg ( Primary products)138 335911 1976:2 - 2005:6 5 Storage battery mfg139 3359291 1976:2 - 2005:6 5 Other communication and energy wire mfg ( Power wire and cable made in plants t140 335932 1976:2 - 2005:6 5 Noncurrent carrying wiring device mfg141 335991p 1976:2 - 2005:6 5 Carbon & graphite product mfg ( Primary products)142 336321p 1976:2 - 2005:6 5 Vehicular lighting equipment mfg ( Primary products)143 337121 1976:2 - 2005:6 5 Upholstered household furniture mfg144 337122 1976:2 - 2005:6 5 Wood household furniture except upholstered145 337124 1976:2 - 2005:6 5 Metal household furniture

42

Name Sample Transformation Description146 337211 1976:2 - 2005:6 5 Wood office furniture mfg147 3372141 1976:2 - 2005:6 5 Nonwood office furniture ( Office seating including upholstered nonwood)148 3399111 1976:2 - 2005:6 5 Jewelry except costume mfg ( Jewelry made of solid platinum metals and solid kar149 3399123 1976:2 - 2005:6 5 Silverware & hollowware mfg ( Flatware and carving sets made wholly of metal)150 339931 1976:2 - 2005:6 5 Doll & stuffed toy mfg151 339932 1976:2 - 2005:6 5 Game toy & children's vehicle mfg152 339944 1976:2 - 2005:6 5 Carbon paper & inked ribbon mfg153 3399931 1976:2 - 2005:6 5 Fastener button needle & pin mfg ( Buttons and parts except for precious or semi154 3399945 1976:2 - 2005:6 5 Broom brush & mop mfg ( Other brushes)

43

D Additional figures

Figure A-1: Figure shows the structural shocks from the FAVAR model for the period1977.3 - 2005.6.

Figure A-2: The figure shows the variables in Ct. The variables are standardized.

44

Figure A-3: Response to a one standard deviation shock to global oil production. 95 percent confidence intervals calculated with bootstrapping method in Kilian (1998).

Figure A-4: Response to a one standard deviation shock in aggregate demand in globalcommodity markets. 95 per cent confidence intervals calculated with bootstrapping methodin Kilian (1998).

45

Figure A-5: Response to a one standard deviation shock to oil-specific demand. 95 percent confidence intervals calculated with bootstrapping method in Kilian (1998).

Figure A-6: Response to a unexpected increase of 25 basis points in the Federal funds rate.95 per cent confidence intervals calculated with bootstrapping method in Kilian (1998).

46

E Robustness

E.1 Different factor combinations

Figure A-7: Robustness with respect to the number of factors. Figure shows response toa one standard deviation shock to global oil production with respectively 1, 3, 5 and 7factors.

Figure A-8: Robustness with respect to the number of factors. Figure shows response toa one standard deviation shock in aggregate demand in global commodity markets withrespectively 1, 3, 5 and 7 factors.

47

Figure A-9: Robustness with respect to the number of factors. Figure shows response to aone standard deviation shock to oil-specific demand with respectively 1, 3, 5 and 7 factors.

Figure A-10: Robustness with respect to the number of factors. Figure shows response toa unexpected increase of 25 basis points in the Federal funds rate with respectively 1, 3, 5and 7 factors.

48

E.2 Different lag length

Figure A-11: Robustness with respect to lag length in equation (1). Figure shows responseto a one standard deviation shock to global oil production with respectively 7, 13, 18 and24 lags.

Figure A-12: Robustness with respect to lag length in equation (1). Figure shows responseto a one standard deviation shock in aggregate demand in global commodity markets withrespectively 7, 13, 18 and 24 lags.

49

Figure A-13: Robustness with respect to lag length in equation (1). Figure shows responseto a one standard deviation shock to oil-specific demand with respectively 7, 13, 18 and24 lags.

Figure A-14: Robustness with respect to lag length in equation (1). Figure shows responseto a unexpected increase of 25 basis points in the Federal funds rate with respectively 7,13, 18 and 24 lags.

50

E.3 Post 1984

Figure A-15: Robustness with respect to starting the estimation in 1984. Figure showsresponse to a one standard deviation shock to global oil production.

Figure A-16: Robustness with respect to starting the estimation in 1984. Figure showsresponse to a one standard deviation shock in aggregate demand in global commoditymarkets.

51

Figure A-17: Robustness with respect to starting the estimation in 1984. Figure showsresponse to a one standard deviation shock to oil-specific demand.

Figure A-18: Robustness with respect to starting the estimation in 1984. Figure showsresponse to a unexpected increase of 25 basis points in the Federal funds rate.

52

E.4 Alternative Identification

Figure A-19: Robustness with respect to an alternative recursive identification where the5 US factors are ordered above the oil factors in the VAR. Figure shows response to aone standard deviation shock to global oil production.

Figure A-20: Robustness with respect to an alternative recursive identification where the5 US factors are ordered above the oil factors in the VAR. Figure shows response to aone standard deviation shock in aggregate demand in global commodity markets.

53

Figure A-21: Robustness with respect to an alternative recursive identification where the5 US factors are ordered above the oil factors in the VAR. Figure shows response to aone standard deviation shock to oil-specific demand.

Figure A-22: Robustness with respect to an alternative recursive identification where the5 US factors are ordered above the oil factors in the VAR. Figure shows response to aunexpected increase of 25 basis points in the Federal funds rate.

54

E.5 Alternative transformation

Figure A-23: Robustness with respect to an alternative transformation where the real oilprice is on first difference in logs. Figure shows response to a one standard deviationshock to global oil production.

Figure A-24: Robustness with respect to an alternative transformation where the real oilprice is on first difference in logs. Figure shows response to a one standard deviationshock in aggregate demand in global commodity markets.

55

Figure A-25: Robustness with respect to an alternative transformation where the real oilprice is on first difference in logs. Figure shows response to a one standard deviationshock to oil-specific demand.

Figure A-26: Robustness with respect to an alternative transformation where the real oilprice is on first difference in logs. Figure shows response to a unexpected increase of 25basis points in the Federal funds rate.

56

E.6 Smaller data set

Figure A-27: Robustness with respect to using a larger data set with disaggregated pricesand consumption. The alternative data set is similar to the data in Boivin, Giannoni,and Mihov (2009). Figure shows response to a one standard deviation shock to global oilproduction.

Figure A-28: Robustness with respect to using a larger data set with disaggregated pricesand consumption. The alternative data set is similar to the data in Boivin, Giannoni,and Mihov (2009). Figure shows response to a one standard deviation shock in aggregatedemand in global commodity markets.

57

Figure A-29: Robustness with respect to using a larger data set with disaggregated pricesand consumption. The alternative data set is similar to the data in Boivin, Giannoni,and Mihov (2009). Figure shows response to a one standard deviation shock to oil-specificdemand.

Figure A-30: Robustness with respect to using a larger data set with disaggregated pricesand consumption. The alternative data set is similar to the data in Boivin, Giannoni,and Mihov (2009). Figure shows response to a unexpected increase of 25 basis points inthe Federal funds rate.

58

F Correlation between variables in the data set and

the common factors

Correlation F1 F2 F3 F4 F5 Δprod rea rpo RF1 1 0 0 0 0 -0.08 0.27 0.70 0.90F2 0 1 0 0 0 -0.05 -0.24 -0.17 -0.17F3 0 0 1 0 0 0.21 -0.34 -0.08 0.13F4 0 0 0 1 0 0.04 0.12 0.37 0.02F5 0 0 0 0 1 0.07 0.24 -0.01 0Δprod -0.08 -0.05 0.21 0.04 0.07 1 -0.02 -0.04 -0.09rea 0.27 -0.25 -0.34 0.12 0.24 -0.02 1 0.41 0.20rpo 0.71 -0.17 -0.08 0.37 0.01 -0.04 0.41 1 0.67R 0.90 0.17 0.13 0.02 0 -0.09 0.20 0.67 1

Table A-5: Correlation between the common factors in Ct.

59

Varia

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prea

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