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Modelling credit growth in commercial banks with the use of data from Senior Loan Officers Opinion Survey

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  • Zuzanna Wośko

Abstract

This paper describes the model of credit growth in the sector of commercial banks in Poland making use of panel data from Senior Loan Officers Opinion Survey (SLOS). The main aim of the model is short-term forecasting of the loan growth at the disaggregated (for particular banks) and aggregated (commercial banks’ sector) level using qualitative information about expected banks’ loan policy. The model was estimated on the sample of quarterly panel data spanning the period from mid- 2005 to 2014 and involving about 30 banks covering more than 80 percent of the Polish banking sector loan portfolio. The model framework includes equations of credit growth in particular segments of loans – corporate, consumer and housing, enhanced by the equations of ordered-choice loan policy, which reflect respondents’ expectations for the next quarter.

Suggested Citation

  • Zuzanna Wośko, 2015. "Modelling credit growth in commercial banks with the use of data from Senior Loan Officers Opinion Survey," NBP Working Papers 210, Narodowy Bank Polski.
    • Handle: RePEc:nbp:nbpmis:210
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    References listed on IDEAS

    as
    1. Lown, Cara & Morgan, Donald P., 2006. "The Credit Cycle and the Business Cycle: New Findings Using the Loan Officer Opinion Survey," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(6), pages 1575-1597, September.
    2. Jeffrey M. Wooldridge, 2005. "Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(1), pages 39-54, January.
    3. Anders Skrondal & Sophia Rabe-Hesketh, 2014. "Handling initial conditions and endogenous covariates in dynamic/transition models for binary data with unobserved heterogeneity," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(2), pages 211-237, February.
    4. Kiviet, Jan F., 1995. "On bias, inconsistency, and efficiency of various estimators in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 68(1), pages 53-78, July.
    5. Judson, Ruth A. & Owen, Ann L., 1999. "Estimating dynamic panel data models: a guide for macroeconomists," Economics Letters, Elsevier, vol. 65(1), pages 9-15, October.
    6. Malgorzata Olszak & Mateusz Pipien & Sylwia Roszkowska & Iwona Kowalska, 2014. "The effects of capital on bank lending in large EU banks – the role of procyclicality, income smoothing, regulations and supervision," Faculty of Management Working Paper Series 52014, University of Warsaw, Faculty of Management.
    7. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
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    More about this item

    Keywords

    credit growth; senior loan officers opinion survey;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • G2 - Financial Economics - - Financial Institutions and Services

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