DETERMINANTS OF NON-GOVERNMENT CREDIT

IN ROMANIA

Student: PĂPURICĂ OANASupervisor: Professor MOISĂ

ALTĂR

Student: PĂPURICĂ OANASupervisor: Professor MOISĂ

ALTĂRBucharestBucharest

JulJuly 2007y 2007

2

ContentsContents

Introduction

Overview of the non-government credit in Romania

Literature review

The empirical model and estimation method

Estimation results

Concluding Remarks

3

GoalsGoals

On the background of recent significant growth of the non-government credit in Romania, this paper attempts to identify the determinants of credit to private non-bank sector during 2003:05 and 2006:12, using Johansen multivariate cointegration analysis and error correction model.

4

Literature ReviewLiterature ReviewAuthors Dependent

variableExplanotory variables Target group Methodology

Hofmann (2001) Real loans Real GDP, real interest rate, housing prices

16 developedcountries

VECM for individualcountries

Calza et al. (2003) Real loans Real GDP growth, nominal lending rate, inflation rate

Eurozone VECM on aggregateeurozone data

Brzoza-Brzezina (2005) Real loans Real GDP growth, real interest rate POR, IRL, GRE,HUN, CZE, POL

VECM for individualcountries

Cottarelli et al. (2005) Credit to the private

sector (%GDP)

Public debt/GDP, PPP-based GDP,inflation threshold,liberalization index, index forentry restrictions to thebanking sector, accountingstandards and legal origin

15 Central Europeanand Balkan countries,out of sampleestimation

Random effect panelestimation of 24developed and

nontransitionemergingcountries

Boissay et al. (2006) Credit to the private

sector (%GDP)

GDP per capita, real interest rate (Euribor), quadratic trend

11 Central andEastern Europeancountries

ECM for individualcountries and panelestimation

Kiss et al. (2006) Credit to the private

Sector (%GDP)

GDP per capita, real interest rate inflation rate

Eurozone Panel estimation

Duenwald et al. (2005) Credit to the private

sector (%GDP)

links with trade balance BLG, ROM, UKR Panel estimation

Gerlach, S. and W. Peng (2003)

Real loans Real GDP, eal property prices Hong Kong VECM

5

Determinants of credit for RomaniaDeterminants of credit for Romania

real non-government credit =

f( economic activity, interest rate, property prices)

6

The facts - RomaniaThe facts - Romania

Non-governmentNon-government real credit growthreal credit growth

48%, 26%, 33%, 47%48%, 26%, 33%, 47%(2003-2006)(2003-2006)

sustained sustained by:by:

• households new loans dynamics households new loans dynamics (360% increase/may03=100) even if their share in(360% increase/may03=100) even if their share intotal credit is still low ( 31%-june 06, 23%-dec06)total credit is still low ( 31%-june 06, 23%-dec06)• consumers loans dynamics (3/4 of households loans)consumers loans dynamics (3/4 of households loans)• foreign exchange denominated loans are preferred foreign exchange denominated loans are preferred (RON appreciation, lower interest rates, (RON appreciation, lower interest rates, prices expressed in euro)prices expressed in euro)• Bucharest is the only place that concentrates a Bucharest is the only place that concentrates a significant percentage of credit (around 40 %); other significant percentage of credit (around 40 %); other counties less than 4 %.counties less than 4 %.

7

Credit economic activityCredit economic activity

Credit DemandCredit Demand

Non-government Non-government credit growthcredit growth47.3% (2006)47.3% (2006)

Credit SupplyCredit Supply

Supported by evolution of Supported by evolution of ::

Economic conditions Economic conditions consumption and investment consumption and investment demand for creditdemand for credit

Changes in economic Changes in economic activity activity firms’ CFs and firms’ CFs and households incomes households incomes ability to repay debts ability to repay debts banks extend creditbanks extend credit

PositivePositive interaction between credit and economic activity interaction between credit and economic activity

8

Credit interest rateCredit interest rate

• Interest rates go Interest rates go up up loans loans become more become more expensive expensive credit demand credit demand reducesreduces

• Monetary tightening (increase in Monetary tightening (increase in interest rates) interest rates) deterioration of deterioration of financial position of firms and financial position of firms and households households reduced reduced creditworthiness creditworthiness credit supply credit supply reduces [balance sheet channel]reduces [balance sheet channel]

• Monetary policy tightening (via Monetary policy tightening (via reduction of banking system liquidity) reduction of banking system liquidity) drain reserves and loanable funds drain reserves and loanable funds reduction of credit supply [bank reduction of credit supply [bank lending channel]lending channel]

Interest rate has a Interest rate has a negativenegative effect both on credit demand and credit supply: effect both on credit demand and credit supply:

Credit demandCredit demand Credit supplyCredit supply

9

Credit property pricesCredit property prices

Credit supplyCredit supply

Property prices may have a Property prices may have a positivepositive effect on both effect on bothcredit demand and credit supply:credit demand and credit supply:

Credit demandCredit demand

• Changes in property prices Changes in property prices wealth effect on credit demand wealth effect on credit demand

• Construction activity depends Construction activity depends positively on the ratio of property positively on the ratio of property prices to construction costs prices to construction costs an an increase in property prices increase in property prices increases construction activity increases construction activity leading to an increase in the leading to an increase in the demand for credit (Tobin’s q-demand for credit (Tobin’s q-

theory of investment)theory of investment)

• Increase in property Increase in property prices prices increases the increases the value of collateralisable value of collateralisable assets assets increases credit increases credit worthiness worthiness banks banks extend creditextend credit

Remark!Remark! a a potential potential two-waytwo-way causality: causality: • increases in credit availability increases in credit availability expand expand the demand for a (temporarily) fixed supply the demand for a (temporarily) fixed supply of properties of properties property prices increase property prices increase

10

Data descriptionData description

Sample: 2003:05 – 2006:12Frequency: monthly

VariableVariable DescriptionDescription

cng_sa Log of real non-government credit, deflated with CPI (index May 2003=100)

ip_sa Log of real industrial output index ( May 2003=100)

ir_l Nominal aggregate lending rate for non-government credit (monthly adjusted)

pp Log of real property prices index ( May 2003=100)

ipc Consumer price index ( May 2003=100)

High seasonality in December month High seasonality in December month cr, ip cr, ip Tramo/Seats Tramo/Seats seasonally adjusted time series cr_sa, ip_sa.seasonally adjusted time series cr_sa, ip_sa.

11

Data descriptionData description

4.6

4.8

5.0

5.2

5.4

5.6

5.8

6.0

2003 2004 2005 2006

CNG_SA

4.56

4.60

4.64

4.68

4.72

4.76

4.80

2003 2004 2005 2006

IP_SA

0.7

0.8

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

2003 2004 2005 2006

IR_L

4.4

4.6

4.8

5.0

5.2

5.4

5.6

2003 2004 2005 2006

PP

12

Unit Root Tests (ADF)Unit Root Tests (ADF)

Symbol Level First difference

cng_sa 0.200259 (T) -11.00894 (C)

ip_sa -0.967589 (C) -9.359537 (C)

ir -0.249589 (C) -11.24778 (C)

pp -1.728586 (T) -2.381833 (N)

C T

1% critical value -3.59 -4.21

5% critical value -2.93 -3.52

10% critical value -2.60 -3.19

13

VAR Lag Order Selection CriteriaVAR Lag Order Selection Criteria

The use of the Johansen procedure implies choosing the appropriate number of lags in VAR. The optimal number of lags in unrestricted VAR was based on the information criteria and LR test.

VAR Lag Order Selection Criteria

Endogenous variables: CNG_SA IP_SA IR_L PP

Exogenous variables: C

Lag LogL LR FPE AIC SC HQ

0 192.9377 NA 9.28E-10 -9.446884 -9.277996 -9.385820

1 341.3865 259.7853 1.24E-12 -16.06932 -15.22488* -15.76400

2 364.7585 36.22667* 8.81E-13* -16.43792* -14.91793 -15.88834*

3 375.0378 13.87708 1.25E-12 -16.15189 -13.95635 -15.35805

4 390.2043 17.44150 1.49E-12 -16.11022 -13.23912 -15.07212

The optimal number of lags in unrestricted VAR has proven to The optimal number of lags in unrestricted VAR has proven to be 2 (equivalently 1 lagged difference in VEC).be 2 (equivalently 1 lagged difference in VEC).

Diagnostics

15

Johansen cointegration analysis Johansen cointegration analysis

Unrestricted Cointegration Rank Test

Hypothesized Trace 5 Percent 1 Percent

No. of CE(s) Eigenvalue Statistic Critical Value Critical Value

None ** 0.597069 64.84857 47.21 54.46

At most 1 0.290309 26.67095 29.68 35.65

At most 2 0.145177 12.26807 15.41 20.04

At most 3 * 0.126490 5.679923 3.76 6.65

*(**) denotes rejection of the hypothesis at the 5%(1%) level

Trace test indicates 1 cointegrating equation(s) at both 5% and 1% levels

Hypothesized Max-Eigen 5 Percent 1 Percent

No. of CE(s) Eigenvalue Statistic Critical Value Critical Value

None ** 0.597069 38.17761 27.07 32.24

At most 1 0.290309 14.40288 20.97 25.52

At most 2 0.145177 6.588145 14.07 18.63

At most 3 * 0.126490 5.679923 3.76 6.65

*(**) denotes rejection of the hypothesis at the 5%(1%) level

Max-eigenvalue test indicates 1 cointegrating equation(s) at both 5% and 1% levels

16

The long-run relationship The long-run relationship

Cointegrating Eq:

CointEq1

CNG_SA(-1) 1.000000

IP_SA(-1) -0.706956

(0.35167)

[-2.01026]

IR_L(-1) 0.943059

(0.08551)

[ 11.0293]

PP(-1) -0.159318

(0.07952)

[-2.00339]

C -2.304933

cng_sa = 0.7069*ip_sa – 0.943*ir_l + 0.1593*pp +2.3049

The long-run elasticity of credit with respect to The long-run elasticity of credit with respect to real industrial productionreal industrial production: : • 1 percent point increase in industrial output 1 percent point increase in industrial output implies an increase of 0.7069 percent points in the implies an increase of 0.7069 percent points in the real credit;real credit;• the null hypothesis that the change of industrial the null hypothesis that the change of industrial production is null in respect to the real credit production is null in respect to the real credit (B(1,2)=0) is rejected (χ2 (1) = 2.96 [0.0849])(B(1,2)=0) is rejected (χ2 (1) = 2.96 [0.0849])• unit output elasticity (B(1,2)=1) is rejected (χ2 unit output elasticity (B(1,2)=1) is rejected (χ2 (1)=11.69 [0.0006])(1)=11.69 [0.0006])The long run semi-elasticity of credit with respect The long run semi-elasticity of credit with respect to to interest rateinterest rate is significantly negative, in is significantly negative, in concordance with economic theory (one percentage concordance with economic theory (one percentage increase in the interest rate triggers a long-run increase in the interest rate triggers a long-run reduction in the real lending of 0.943 percent).reduction in the real lending of 0.943 percent).The elasticity of credit with respect to The elasticity of credit with respect to property property pricesprices is significant positive (One percentage is significant positive (One percentage increase in the property prices has a 0.15 percent increase in the property prices has a 0.15 percent increase in the real credit)increase in the real credit)

17

The long-run relationshipThe long-run relationship

Error Correction: D(CNG_SA) D(IP_SA) D(IR_L) D(PP)

CointEq1 -0.247214 0.047480 -0.623764 0.017794

(0.13870) (0.06760) (0.12485) (0.04046)

[-1.78239] [ 0.70242] [-4.99626] [ 0.43976]

The coefficient representing the The coefficient representing the speed of adjustmentspeed of adjustment of real credit of real credit indicates: indicates:

-relatively rapid adjustment of real credit to the long-run equilibrium; relatively rapid adjustment of real credit to the long-run equilibrium;

-if in the previous month the real credit exceeded the long-run level if in the previous month the real credit exceeded the long-run level in the current month real credit would decrease (negative sign);in the current month real credit would decrease (negative sign);

- the disequilibria accommodates relatively quickly: 25% from the - the disequilibria accommodates relatively quickly: 25% from the previous month disequilibrium is adjusted in the current month previous month disequilibrium is adjusted in the current month 4months.4months.

18

The long-run relationshipThe long-run relationship

We computed the series of residuals from the long-run equilibrium relationship and tested the resulting series for stationarity resid01 I(0); the cointegrating equation represents indeed a long-run run relationship between the specified variables.

resid01=1*cng_sa-0.706956*ip_sa+0.943059*ir_l-0.159318*pp-2.30493

-.3

-.2

-.1

.0

.1

.2

2003 2004 2005 2006

Cointegrating relation 1

These deviations from the long-run equilibrium These deviations from the long-run equilibrium are stationary and we are going to use them in are stationary and we are going to use them in an error correction mechanism. These an error correction mechanism. These deviations try to adjust to the equilibrium at the deviations try to adjust to the equilibrium at the end of the period, but there is a decrease of end of the period, but there is a decrease of these deviations in September 2005 when came these deviations in September 2005 when came into force the restrictive provisions of Norm 10 into force the restrictive provisions of Norm 10 on mitigating credit risk for credit granted to on mitigating credit risk for credit granted to individuals individuals

19

Weak-exogeneity tests Weak-exogeneity tests

Δ cng_saA(1,1)=0

Δ ip_saA(2,1)=0

Δ ir_lA(3,1)=0

ΔppA(4,1)=0

Δ ip_sa, ΔppA(2,1)=0,A(4,1)=0

χ2 (1) = 3.1293 [0.0768]

χ2 (1) = 0.4611 [0.4970]

χ2 (1) = 18.787 [0.000015]

χ2 (1) = 0.1699 [0.6801]

χ2 (1) = 0.9470 [0.622811]

The weak exogeneity hypothesis is accepted The weak exogeneity hypothesis is accepted both separately and jointlyboth separately and jointly for for industrial output and property prices. It is rejected for the interest rate. The industrial output and property prices. It is rejected for the interest rate. The interest rate is not weak exogenous and it adjusts to the real lending interest rate is not weak exogenous and it adjusts to the real lending disequilibria from the long term level. disequilibria from the long term level.

The hypothesis that industrial production deviation form the equilibrium The hypothesis that industrial production deviation form the equilibrium level does not adjust to the other variables included in the cointegration level does not adjust to the other variables included in the cointegration relationship is accepted with a probability of 49.7%. relationship is accepted with a probability of 49.7%.

Weak exogeneity hypothesis of property prices suggests that property prices Weak exogeneity hypothesis of property prices suggests that property prices are determined outside the system, they are not caused by real credit, but they are determined outside the system, they are not caused by real credit, but they determine real credit. So is not the rise in real credit that generates an increase determine real credit. So is not the rise in real credit that generates an increase

of property prices but real credit increases in order to reach the equilibrium.of property prices but real credit increases in order to reach the equilibrium.

Note: The null hypothesis is that there is weak exogeneity (in squared brackets - Note: The null hypothesis is that there is weak exogeneity (in squared brackets - probability)probability)

20

Short run error correction model (ECM) Short run error correction model (ECM)

The deviation of real credit from its long-run level is stationary, so we can use it in an error correction mechanism (the residual series will be used as error correction term in dynamic model)

D(CNG_SA)= C(1)*D(CNG_SA(-1))+ C(2)*D(IP_SA(-1)) + C(3)*D(IR_L(1))+ C(4)*D(PP(-1))+C(5)* RESID01(-1) +C(6)

21

Short run error correction model (ECM)Short run error correction model (ECM)

Following the general-to-specific approach, we can obtain a parsimonious model:

Dependent Variable: D(CNG_SA)

Method: Least Squares

Sample(adjusted): 2003:07 2006:12

D(CNG_SA)= C(1)*D(CNG_SA(-1))+ C(2)*D(IP_SA(-1)) + C(5)* RESID01(-1) +C(6)

Coefficient Std. Error t-Statistic Prob.

C(1) -0.335399 0.127261 -2.635529 0.0121

C(2) -1.017459 0.327678 -3.105055 0.0036

C(5) -0.216727 0.129821 -1.669434 0.1033

C(6) 0.035997 0.009107 3.952909 0.0003

R-squared 0.428313 Mean dependent var 0.022608

Adjusted R-squared 0.383180 S.D. dependent var 0.069881

S.E. of regression 0.054883 Akaike info criterion -2.876841

Sum squared resid 0.114461 Schwarz criterion -2.711348

Log likelihood 64.41365 Durbin-Watson stat 2.263962

22

Short run error correction model (ECM)Short run error correction model (ECM)

In the short-run one lag changes in interest rates and property prices are not significant for the current real credit growth.

The error correction term has a negative sign but is significant at 10 percent level. This sign suggests that in the current month real credit adjusts as a result of previous month disequilibrium from the equilibrium level.

When credit departs from its long-term trend, the adjustment towards equilibrium implies not only a change in credit, but also a change in industrial production. More specifically, when lending is above (below) its long-run level, restoring equilibrium is achieved via reductions (increases) in lending, but also a contraction (expansion) of industrial output.

23

Concluding remarksConcluding remarks

Cointegration analysis reveals that there is a stationary long run relationship between real non-government credit, industrial production as a proxy for the economic activity, nominal interest rate and real property prices.

An important finding of this paper is that property prices can be considered a determinant of credit in the long-run.

Property prices weak exogenous : are determined outside the system, they are not caused by real credit, but they determine real credit.

The coefficient representing the speed of adjustment of real credit indicates relatively rapid adjustment of real credit to the long-run equilibrium ( aprox 4 months).

24

Concluding remarksConcluding remarks

In the short-run real credit is not influenced by changes in property prices and interest rates and when credit departs from its long-term trend, the adjustment towards equilibrium implies not only a change in credit, but also a change in industrial production.

Limitations:- The estimated elasticities must be used cautiously, as it is difficult to interpret them as true long-run elasticities given the short time series available (44 observations).- The inexistence of an official property price index.- The use of industrial output as a proxy for economic activity.- The monthly series.

25

Further researchFurther research

For further research, we should consider an analysis on the components of non-government credit and include a bigger number of determinants (as unemployment rate for credit to individuals, exchange rate, consumption, wages, etc).

An important aspect to be considered for the further research could be the new regulatory framework starting with 2007 imposed by Regulation Nr. 3/2007 which comes with relaxing credit conditions for households.

26

References References

Boissay, F., O. Calvo-Gonzalez and T. Kozluk (2006), “Is Lending in Central and Eastern Europe Developing too Fast? “ Paper presented at the Conference.Finance and Consumption Workshop: Consumption and Credit in Countries with Developing Credit Markets., Florence, 16-17 June 2006.

Brzoza-Brzezina, M. ( 2005), “Lending booms in Europe.s periphery: South- Western lessons for Central-Eastern members”, ECB Working Paper no. 543.

Calza, A., C. Gartner, and J. Sousa (2003), “Modelling the Demand for Loans to the Private Sector in the Euro Area”, Applied Economics, 35, pp. 107-117.

Cottarelli, C., G. Dell.Ariccia and I. Vladkova-Hollar (2005), “Early birds, late risers and sleeping beauties: Bank credit growth to the private sector in Central and Eastern Europe and in the Balkans”. Journal of Banking and Finance. 29, 83-104.

Dickey, D and W Fuller (1981), “Likelihood ratio statistics for autoregressive time series with a unit root”, Econometrica, 60, pp 423-33.

Duenwald, C., N. Gueorguiev, and A. Schaechter (2005). “Too much of a good thing? Credit booms in transition economies: The cases of Bulgaria, Romania, and Ukraine.” IMF Working Paper no. 128.

Egert, B. , P. Barke, and T. Zumer (2006), “Credit growth in Central and Eastern Europe. New (Over) Shooting stars?”, European Central Bank Working Paper No. 687

Engle, R. and C. Granger (1987), “Cointegration and Error-correction: Representation, Estimation and Testing”, Econometrica, 55, 252-276.

Ericsson, N. R. (1992), “Cointegration, exogeneity, and policy analysis: an overview”, Journal of policy modeling 14(3): 251-280

Gerlach, S. and W. Peng (2003), “Bank Lending and Property Prices in Hong Kong” HKIMR Working Paper No. 12/2003.

Greene, W. (1993), “Econometric Analysis”, second edition, Macmillan Publishing Company, New York, N.Y.

Hall, S., (2001) “Credit channel effects in the monetary transmission mechanism” Bank of England Quarterly Bulletin Q4. 442.448.

Hendry, F. D., and K. Juselius (2000), “Explaining Cointegration Analysis: Part II”, Energy Journal 21

27

ReferencesReferences

Hofmann, B (2003), “Bank Lending and Property Prices: Some International Evidence”,The Hong Kong Institute for Monetary Research Working Paper, No. 22.

(2001), “The determinants of private sector credit in industrialized countries: Do property prices matter?” BIS Working Paper 108.

Hamilton, J (1994), “Time series analysis”, Princeton University Press, Princeton, New Jersey. Johansen S., Juselius K., (1990), “Maximum Likelihood Estimation and Inference on Cointegration - with

Applications to Simultaneous Equations and Cointegration”, Journal of Econometrics, 69 Johansen, S (1988), “Statistical analysis of cointegration vectors”, Journal of Economic Dynamics and

Control, 12, pp 231-54. (1991), “Estimation and hypothesis testing of cointegration vectors in gaussian vector autoregressive

models”, Econometria, 59, pp 1551-81. Kiss, G., M. Nagy and B. Vonnák (2006), “Credit Growth in Central and Eastern Europe: Trend, Cycle or

Boom?” Paper presented at the conference Finance and Consumption Workshop: Consumption and Credit in Countries with Developing Credit Markets., Florence, 16-17 June.

MacKinnon, J.G. (1996), “Numerical Distribution Functions for Unit Root and Cointegration Tests,” Journal of Applied Econometrics, 11, 601-618.

Martinez-Carrascal, C. and A. del Rio (2004), “Households borrowing and consumption in spain: a VECM approach”, Banca de Espania, Documentos de Trabajo no. 0421

Osterwald-Lenum, M (1992), “A note with quantiles of the asymptotic distribution of the maximum likelihood cointegration rank test statistics”, Oxford Bulletin of Economics and Statistics, 54, pp 461-72.

Sims, C (1980), “Macroeconomics and reality”, Econometrica, 48, pp 1-48. Terrones, M. and E. Mendoza (2004), “Are credit booms in emerging markets a concern?” IMF, World

Economic Outlook, Chapter IV. 147-166. Tobin, J (1969), “A general equilibrium approach to monetary theory”, Journal of Money, Credit and

Banking, 1, pp 15-29. *** ECB Monthly Bulletin 11/2006 *** NBR Monthly Bulletin 2003-2006 *** NBR Annual Reports 2003-2006 *** NBR Financial Stability Report 2006-2007

28

Thank you!