DETERMINANTS OF NON-GOVERNMENT CREDIT IN ROMANIA

1. DETERMINANTS OF NON-GOVERNMENT CREDIT IN ROMANIA Student: PĂPURICĂ OANA Supervisor: Professor MOISĂ ALTĂR Bucharest July 2007

2. Contents • Introduction • Overview of the non-government credit in Romania • Literature review • The empirical model and estimation method • Estimation results • Concluding Remarks

3. Goals • 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 Review

5. Determinants of credit for Romania real non-government credit = f( economicactivity, interest rate, property prices)

6. The facts - Romania Non-government real credit growth 48%, 26%, 33%, 47% (2003-2006) sustained by: • households new loans dynamics • (360% increase/may03=100) even if their share in • total credit is still low ( 31%-june 06, 23%-dec06) • consumers loans dynamics (3/4 of households loans) • foreign exchange denominated loans are preferred • (RON appreciation, lower interest rates, • prices expressed in euro) • Bucharest is the only place that concentrates a • significant percentage of credit (around 40 %); other • counties less than 4 %.

7. Non-government credit growth 47.3% (2006) Credit Demand Credit Supply Credit  economic activity Supported by evolution of : Changes in economic activity  firms’ CFs and households incomes  ability to repay debts  banks extend credit Economic conditions  consumption and investment  demand for credit Positive interaction between credit and economic activity

8. Interest rate has a negative effect both on credit demand and credit supply: Credit demand Credit supply Credit  interest rate • Monetary tightening (increase in interest rates) deterioration of financial position of firms and households  reduced creditworthiness  credit supply reduces [balance sheet channel] • Monetary policy tightening (via reduction of banking system liquidity)  drain reserves and loanable funds  reduction of credit supply [bank lending channel] • Interest rates go up  loans become more expensive  credit demand reduces

9. Credit  property prices Property prices may have a positive effect on both credit demand and credit supply: Credit demand Credit supply • Changes in property prices  wealth effect on credit demand • Construction activity depends positively on the ratio of property prices to construction costs  an increase in property prices  increases construction activity  leading to an increase in the demand for credit (Tobin’s q-theory of investment) • Increase in property prices  increases the value of collateralisable assets  increases credit worthiness  banks extend credit • Remark! apotential two-way causality: • increases in credit availability  expand • the demand for a (temporarily) fixed supply • of properties  property prices increase

10. Data description Sample: 2003:05 – 2006:12 Frequency: monthly High seasonality in December month  cr, ip  Tramo/Seats  seasonally adjusted time series cr_sa, ip_sa.

11. Data description

12. Unit Root Tests (ADF)

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

14. Johansen cointegration analysis

15. The long-run relationship cng_sa = 0.7069*ip_sa – 0.943*ir_l + 0.1593*pp +2.3049 • The long-run elasticity of credit with respect to real industrial production: • 1 percent point increase in industrial output implies an increase of 0.7069 percent points in the real credit; • the null hypothesis that the change of industrial production is null in respect to the real credit (B(1,2)=0) is rejected (χ2 (1) = 2.96 [0.0849]) • unit output elasticity (B(1,2)=1) is rejected (χ2 (1)=11.69 [0.0006]) • The long run semi-elasticity of credit with respect to interest rate is significantly negative, in concordance with economic theory (one percentage increase in the interest rate triggers a long-run reduction in the real lending of 0.943 percent). • The elasticity of credit with respect to property prices is significant positive (One percentage increase in the property prices has a 0.15 percent increase in the real credit)

16. The long-run relationship • The coefficient representing the speed of adjustment of real credit indicates: • relatively rapid adjustment of real credit to the long-run equilibrium; • if in the previous month the real credit exceeded the long-run level in the current month real credit would decrease (negative sign); • - the disequilibria accommodates relatively quickly: 25% from the previous month disequilibrium is adjusted in the current month 4months.

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

18. Weak-exogeneity tests Note: The null hypothesis is that there is weak exogeneity (in squared brackets - probability) • The weak exogeneity hypothesis is accepted both separately and jointly for 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 disequilibria from the long term level. • The hypothesis that industrial production deviation form the equilibrium level does not adjust to the other variables included in the cointegration relationship is accepted with a probability of 49.7%. • Weak exogeneity hypothesis of property prices suggests that property prices 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 of property prices but real credit increases in order to reach the equilibrium.

19. 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)

20. Short run error correction model (ECM) • Following the general-to-specific approach, we can obtain a parsimonious model:

21. 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.

22. Concluding 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).

23. Concluding 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.

24. Further 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.

25. 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

26. References • 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

27. Thank you!