IDEAS home Printed from https://ideas.repec.org/p/anc/wpaper/441.html
   My bibliography  Save this paper

Penalized Maximum Likelihood Estimation Of Logit-Based Early Warning Systems

Author

Listed:
  • Claudia Pigini

    (Dipartimento di Scienze Economiche e Sociali - Universita' Politecnica delle Marche)

Abstract

Panel logit models have proved to be simple and effective tools to build Early Warning Systems (EWS) for financial crises. But because crises are rare events, the estimation of EWS does not usually account for country fixed effects, so as to avoid losing all the information relative to countries that never face a crisis. I propose using a penalized maximum likelihood estimator for fixed-effects logit-based EWS where all the observations are retained. I show that including country effects, while preserving the entire sample, greatly improves the predictive power of EWS with respect to the pooled, random-effects and standard fixed-effects models.

Suggested Citation

  • Claudia Pigini, 2019. "Penalized Maximum Likelihood Estimation Of Logit-Based Early Warning Systems," Working Papers 441, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    • Handle: RePEc:anc:wpaper:441
    as

    Download full text from publisher

    File URL: http://docs.dises.univpm.it/web/quaderni/pdf/441.pdf
    File Function: First version, 2019
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Ioannis Kosmidis & David Firth, 2009. "Bias reduction in exponential family nonlinear models," Biometrika, Biometrika Trust, vol. 96(4), pages 793-804.
    2. Asli Demirgüç-Kunt & Enrica Detragiache, 2005. "Cross-Country Empirical Studies of Systemic Bank Distress: A Survey," National Institute Economic Review, National Institute of Economic and Social Research, vol. 192(1), pages 68-83, April.
    3. Francisco J Valverde-Albacete & Carmen Peláez-Moreno, 2014. "100% Classification Accuracy Considered Harmful: The Normalized Information Transfer Factor Explains the Accuracy Paradox," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-10, January.
    4. Chamberlain, Gary, 1982. "The General Equivalence of Granger and Sims Causality," Econometrica, Econometric Society, vol. 50(3), pages 569-581, May.
    5. Antunes, António & Bonfim, Diana & Monteiro, Nuno & Rodrigues, Paulo M.M., 2018. "Forecasting banking crises with dynamic panel probit models," International Journal of Forecasting, Elsevier, vol. 34(2), pages 249-275.
    6. Lorenzo Cappellari & Stephen P. Jenkins, 2004. "Modelling low income transitions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(5), pages 593-610.
    7. Dawood, Mary & Horsewood, Nicholas & Strobel, Frank, 2017. "Predicting sovereign debt crises: An Early Warning System approach," Journal of Financial Stability, Elsevier, vol. 28(C), pages 16-28.
    8. Caggiano, Giovanni & Calice, Pietro & Leonida, Leone & Kapetanios, George, 2016. "Comparing logit-based early warning systems: Does the duration of systemic banking crises matter?," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 104-116.
    9. 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.
    10. Moritz Schularick & Alan M. Taylor, 2012. "Credit Booms Gone Bust: Monetary Policy, Leverage Cycles, and Financial Crises, 1870-2008," American Economic Review, American Economic Association, vol. 102(2), pages 1029-1061, April.
    11. Kunz, Johannes S. & Staub, Kevin E. & Winkelmann, Rainer, 2017. "Estimating Fixed Effects: Perfect Prediction and Bias in Binary Response Panel Models, with an Application to the Hospital Readmissions Reduction Program," IZA Discussion Papers 11182, Institute of Labor Economics (IZA).
    12. Mr. Markus Eberhardt & Mr. Andrea F Presbitero, 2018. "Commodity Price Movements and Banking Crises," IMF Working Papers 2018/153, International Monetary Fund.
    13. Jinyong Hahn & Whitney Newey, 2004. "Jackknife and Analytical Bias Reduction for Nonlinear Panel Models," Econometrica, Econometric Society, vol. 72(4), pages 1295-1319, July.
    14. Amemiya, Takeshi, 1981. "Qualitative Response Models: A Survey," Journal of Economic Literature, American Economic Association, vol. 19(4), pages 1483-1536, December.
    15. Gary Chamberlain, 1980. "Analysis of Covariance with Qualitative Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 225-238.
    16. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    17. Hsiao,Cheng, 2015. "Analysis of Panel Data," Cambridge Books, Cambridge University Press, number 9781107038691.
    18. Mr. Luc Laeven & Mr. Fabian Valencia, 2018. "Systemic Banking Crises Revisited," IMF Working Papers 2018/206, International Monetary Fund.
    19. Davis, E. Philip & Karim, Dilruba, 2008. "Comparing early warning systems for banking crises," Journal of Financial Stability, Elsevier, vol. 4(2), pages 89-120, June.
    20. James J. Heckman, 1981. "Heterogeneity and State Dependence," NBER Chapters, in: Studies in Labor Markets, pages 91-140, National Bureau of Economic Research, Inc.
    21. Asli Demirgüç-Kunt & Enrica Detragiache, 1998. "The Determinants of Banking Crises in Developing and Developed Countries," IMF Staff Papers, Palgrave Macmillan, vol. 45(1), pages 81-109, March.
    22. Lorenzo Cappellari & Stephen P. Jemkins, 2002. "Who Stays Poor? Who Becomes Poor? Evidence from the British Household Panel Survey," Economic Journal, Royal Economic Society, vol. 112(478), pages 60-67, March.
    23. Ghulam, Yaseen & Derber, Julian, 2018. "Determinants of sovereign defaults," The Quarterly Review of Economics and Finance, Elsevier, vol. 69(C), pages 43-55.
    24. Lancaster, Tony, 2000. "The incidental parameter problem since 1948," Journal of Econometrics, Elsevier, vol. 95(2), pages 391-413, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kajal Lahiri & Cheng Yang, 2023. "ROC and PRC Approaches to Evaluate Recession Forecasts," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(2), pages 119-148, September.
    2. Francesco Bartolucci & Claudia Pigini & Francesco Valentini, 2024. "MCMC conditional maximum likelihood for the two-way fixed-effects logit," Econometric Reviews, Taylor & Francis Journals, vol. 43(6), pages 379-404, July.

    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. Pigini, Claudia & Bartolucci, Francesco, 2022. "Conditional inference for binary panel data models with predetermined covariates," Econometrics and Statistics, Elsevier, vol. 23(C), pages 83-104.
    2. Francesco Bartolucci & Claudia Pigini, 2017. "Granger causality in dynamic binary short panel data models," Working Papers 421, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    3. Chung‐Hua Shen & Hsing‐Hua Hsu, 2022. "The determinants of Asian banking crises—Application of the panel threshold logit model," International Review of Finance, International Review of Finance Ltd., vol. 22(1), pages 248-277, March.
    4. Lionel WILNER, 2019. "The Dynamics of Individual Happiness," Working Papers 2019-18, Center for Research in Economics and Statistics.
    5. Manudeep Bhuller & Christian N. Brinch & Sebastian Königs, 2017. "Time Aggregation and State Dependence in Welfare Receipt," Economic Journal, Royal Economic Society, vol. 127(604), pages 1833-1873, September.
    6. Francesco Bartolucci & Claudia Pigini & Francesco Valentini, 2023. "Conditional inference and bias reduction for partial effects estimation of fixed-effects logit models," Empirical Economics, Springer, vol. 64(5), pages 2257-2290, May.
    7. Fernández-Val, Iván & Vella, Francis, 2011. "Bias corrections for two-step fixed effects panel data estimators," Journal of Econometrics, Elsevier, vol. 163(2), pages 144-162, August.
    8. Francesco Bartolucci & Claudia Pigini & Francesco Valentini, 2024. "MCMC conditional maximum likelihood for the two-way fixed-effects logit," Econometric Reviews, Taylor & Francis Journals, vol. 43(6), pages 379-404, July.
    9. Lucchetti, Riccardo & Pigini, Claudia, 2017. "DPB: Dynamic Panel Binary Data Models in gretl," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i08).
    10. Casabianca, Elizabeth Jane & Catalano, Michele & Forni, Lorenzo & Giarda, Elena & Passeri, Simone, 2022. "A machine learning approach to rank the determinants of banking crises over time and across countries," Journal of International Money and Finance, Elsevier, vol. 129(C).
    11. William H. Greene & David A. Hensher, 2008. "Modeling Ordered Choices: A Primer and Recent Developments," Working Papers 08-26, New York University, Leonard N. Stern School of Business, Department of Economics.
    12. Mathonnat, Clément & Minea, Alexandru, 2018. "Financial development and the occurrence of banking crises," Journal of Banking & Finance, Elsevier, vol. 96(C), pages 344-354.
    13. Huynh, Tran & Uebelmesser, Silke, 2024. "Early warning models for systemic banking crises: Can political indicators improve prediction?," European Journal of Political Economy, Elsevier, vol. 81(C).
    14. Filippopoulou, Chryssanthi & Galariotis, Emilios & Spyrou, Spyros, 2020. "An early warning system for predicting systemic banking crises in the Eurozone: A logit regression approach," Journal of Economic Behavior & Organization, Elsevier, vol. 172(C), pages 344-363.
    15. Xianglong Liu, 2023. "Towards Better Banking Crisis Prediction: Could an Automatic Variable Selection Process Improve the Performance?," The Economic Record, The Economic Society of Australia, vol. 99(325), pages 288-312, June.
    16. Aguirregabiria, Victor & Gu, Jiaying & Luo, Yao, 2021. "Sufficient statistics for unobserved heterogeneity in structural dynamic logit models," Journal of Econometrics, Elsevier, vol. 223(2), pages 280-311.
    17. Audit, Dooneshsingh & Alam, Nafis, 2022. "Why have credit variables taken centre stage in predicting systemic banking crises?," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 3(1).
    18. Sara Ayllón, 2009. "Modelling State Dependence and Feedback Effects between Poverty, Employment and Parental Home Emancipation among European Youth," SOEPpapers on Multidisciplinary Panel Data Research 235, DIW Berlin, The German Socio-Economic Panel (SOEP).
    19. Brandão-Marques, Luis & Chen, Qianying & Raddatz, Claudio & Vandenbussche, Jérôme & Xie, Peichu, 2022. "The riskiness of credit allocation and financial stability," Journal of Financial Intermediation, Elsevier, vol. 51(C).
    20. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).

    More about this item

    Keywords

    Keywords: Banking Crisis; Bias Reduction; Fixed-Effects Logit; Separated Data;
    All these 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
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:anc:wpaper:441. 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: Maurizio Mariotti (email available below). General contact details of provider: https://edirc.repec.org/data/deancit.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.