Estimating Credit Demand in Croatia

1. Estimating Credit Demand in Croatia By Katja Gattin-Turkalj, Igor Ljubaj, Ana Martinis, Marko Mrkalj Discussant: K. Žigić Prague, Czech Republic

2. Research Questions How do demand determinants explain the credit growth in Croatia? Was credit growth excessive during the past 10 years? Does recent boom in property prices help explain credit demand in Croatia? Importance: macroeconomic stability (inflation, foreign debt, potential fragility of the banking sector) Demand for total loans to the private sector versus households demand for loans

3. Stylized Facts about Loans Developments • Three lending booms • Increased importance of leasing after HNB regulatory measures

4. Modeling • Basic model: Loans = f( r, GDP) • Extensions: Loans = f( r, GDP, Loans_1, X) • OLS Econometrics problems: (trend versus first-difference stationary data, endogeneity/ causality issues, omitted variables, multicolinearity, serial correlation)

5. Results 1. The baseline specification seems to explain well the observed developments of credit; 2. The extension of the baseline equation, including the variables used to control for the supply specific factors, did not significantly change the results; in particular, they did not change the assessment of periods of “excessive” credit growth vs. periods when the credit growth was in line with the fundamentals.

6. Results 3. As for household demand for credit, the results show that the coefficient on the interest rates on household loans was negative in all specifications (although non-significant in the specifications containing lagged depending variable); 4. House price index did not contribute to explaining household credit demand.

7. Comments, Suggestions, Questions Why not try a simple structural supply-demand model for loans? (Only if banking sector can be characterized as monopolistically competitive, estimation of demand is not a problem!) …or at least demand for different categories of loans For instance, demand for leasing versus demand for an ordinary loan (different monetary implications of these two modes): Loans = f( rlo, rle ,GDP) Leasing = g( rle, rlo ,GDP)

8. Comments, Suggestions, Questions • Another promising possibility is to disaggregate household demand into its components and look deeper into its determinants (demographic factors, size, state and age of the vehicle stock, etc. ) • Estimate demand system for components of household demand! • Based on Fig 4, (page 9) one would expect that wealth has strong impact on the demand for households loans.

9. Comments, Suggestions, Questions • “ More formally, assumptions on exogeneity “determined outside the system under analysis“ and causality in the Granger sense "presence/absence of feedback between variables“ (Hendry, 1995) are made. Our expectation is that, although supply side plays a significant role in financial sector transition, we can still learn about the demand specific factors and their influence on credit growth.” (Page 11) • What is the meaning of the section above? It seems that there was no Granger causality testing but just assumption of exogenaty among variables

10. Comments, Suggestions, Questions • “Supply effects have traditionally played more prominent role in recent credit booms in the CEE and NMS, and therefore, literature on "credit booms" and "lending channel" has predominantly been focusing on the supply side factors.” (page 1) How do you know that? References? • Therefore, although credit demand offers only partial explanation of credit developments and hinges on the assumption on the limited supply effects, an expanding strand of recent credit literature focuses on the demand factors alone. (page 1) (Again!)How do you know that? References?

11. Comments, Suggestions, Questions Be more specific on how you measure the “excessiveness” of credit growth. It seems, by checking if actual values exceed the ones predicted by the model. Is this sufficient? Out of sample prediction has to be used here from some other country! What is the rationale for consumption to be used as regressor? (Perhaps, it serves as a good proxy for household income!) Define the notion of “GAP” used in the text (from table footnote, the reader can learn that gaps are defined as the deviation from H-P trend. So content of Fig. 5 and 9 have to be better explained!) Finally, when using abbreviations like, for instance, ADF then indicate them in the parentheses after the first use of it: e.g. Augmented Dickey-Fuller (ADF). (Also H-P was nowhere defined, Hodrick-Presoctt filter, I presume)