IDEAS home Printed from https://ideas.repec.org/p/nbp/nbpmis/210.html

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://static.nbp.pl/publikacje/materialy-i-studia/210_en.pdf
    Download Restriction: no
    ---><---

    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. 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.
    6. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    7. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sangyup Choi & Kimoon Jeong & Jiseob Kim, 2023. "One Monetary Policy and Two Bank Lending Standards: A Tale of Two Europes," Working papers 2023rwp-209, Yonsei University, Yonsei Economics Research Institute.
    2. Eicher, Theo S. & Schreiber, Till, 2010. "Structural policies and growth: Time series evidence from a natural experiment," Journal of Development Economics, Elsevier, vol. 91(1), pages 169-179, January.
    3. Garita, Gus, 2009. "How Does Financial Openness Affect Economic Growth and its Components?," MPRA Paper 20099, University Library of Munich, Germany.
    4. Vinayagathasan, Thanabalasingam, 2013. "Inflation and economic growth: A dynamic panel threshold analysis for Asian economies," Journal of Asian Economics, Elsevier, vol. 26(C), pages 31-41.
    5. Erkan Erdil & I. Hakan Yetkiner, 2009. "The Granger-causality between health care expenditure and output: a panel data approach," Applied Economics, Taylor & Francis Journals, vol. 41(4), pages 511-518.
    6. Elisa Giuliani & Federica Nieri & Andrea Vezzulli, 2019. "BEST IN CLASS BUT BIG WRONGDOERS: Exploring the financial performance and human rights infringe ments nexus in large emerging country companies," Discussion Papers 2019/250, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    7. Dang, Rey & Houanti, L'Hocine & Sahut, Jean-Michel & Simioni, Michel, 2021. "Do women on corporate boards influence corporate social performance? A control function approach," Finance Research Letters, Elsevier, vol. 39(C).
    8. Martin A. Carree, 2002. "Nearly Unbiased Estimation in Dynamic Panel Data Models with Exogenous Variables," Tinbergen Institute Discussion Papers 02-007/2, Tinbergen Institute.
    9. Boeri, Tito & Brücker, Herbert, 2005. "Migration, Co-ordination Failures and EU Enlargement," IZA Discussion Papers 1600, IZA Network @ LISER.
    10. Chris Belmert Milindi & Roula Inglesi-Lotz, 2023. "Impact of technological progress on carbon emissions in different country income groups," Energy & Environment, , vol. 34(5), pages 1348-1382, August.
    11. Brücker, Herbert & Siliverstovs, Boriss, 2006. "Estimating and forecasting European migration : methods, problems and results," Zeitschrift für ArbeitsmarktForschung - Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 39(1), pages 35-56.
    12. Karsten Staehr & Katri Urke, 2022. "The European structural and investment funds and public investment in the EU countries," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 49(4), pages 1031-1062, November.
    13. Mansor H. Ibrahim, 2018. "Trade–finance complementarity and carbon emission intensity: panel evidence from middle-income countries," Environment Systems and Decisions, Springer, vol. 38(4), pages 489-500, December.
    14. Everaert, Gerdie & Jansen, Stijn, 2018. "On the estimation of panel fiscal reaction functions: Heterogeneity or fiscal fatigue?," Economic Modelling, Elsevier, vol. 70(C), pages 87-96.
    15. Issiaka Coulibaly & Blaise Gnimassoun, 2013. "Current account sustainability in Sub-Saharan Africa: Does the exchange rate regime matter?," Working Papers hal-04141160, HAL.
    16. Catherine Hausman, 2012. "Biofuels and Land Use Change: Sugarcane and Soybean Acreage Response in Brazil," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 51(2), pages 163-187, February.
    17. Hao, Yu & Zhang, Zong-Yong & Liao, Hua & Wei, Yi-Ming, 2015. "China’s farewell to coal: A forecast of coal consumption through 2020," Energy Policy, Elsevier, vol. 86(C), pages 444-455.
    18. Mahir Binici & Yin-Wong Cheung & Kon S. Lai, 2011. "Trade Openness, Market Competition, and Inflation: Some Sectoral Evidence from OECD Countries," CESifo Working Paper Series 3690, CESifo.
    19. Li, Hongchang & Strauss, Jack & Shunxiang, Hu & Lui, Lu, 2018. "Do high-speed railways lead to urban economic growth in China? A panel data study of China’s cities," The Quarterly Review of Economics and Finance, Elsevier, vol. 69(C), pages 70-89.
    20. Herbert Brücker & Philipp Schröder, 2007. "EU accession and the hardening of soft budget constraints: some macro evidence," Economic Change and Restructuring, Springer, vol. 40(3), pages 235-252, September.

    More about this item

    Keywords

    ;
    ;

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbp:nbpmis:210. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Jakub Growiec (email available below). General contact details of provider: https://edirc.repec.org/data/nbpgvpl.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.