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On factors explaining the 2008 financial crisis

Author

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  • Acosta-González, Eduardo
  • Fernández-Rodríguez, Fernando
  • Sosvilla-Rivero, Simón

Abstract

Using a statistical methodology guided by a genetic algorithm, we select the best econometric model for explaining the severity of the 2008 crisis, with the main determinant being the percentage of bank claims on private sector over deposits in the year 2006.

Suggested Citation

  • Acosta-González, Eduardo & Fernández-Rodríguez, Fernando & Sosvilla-Rivero, Simón, 2012. "On factors explaining the 2008 financial crisis," Economics Letters, Elsevier, vol. 115(2), pages 215-217.
    • Handle: RePEc:eee:ecolet:v:115:y:2012:i:2:p:215-217
    DOI: 10.1016/j.econlet.2011.11.038
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    References listed on IDEAS

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    1. Kevin D. Hoover & Stephen J. Perez, 1999. "Data mining reconsidered: encompassing and the general-to-specific approach to specification search," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 167-191.
    2. Lovell, Michael C, 1983. "Data Mining," The Review of Economics and Statistics, MIT Press, vol. 65(1), pages 1-12, February.
    3. Eduardo Acosta-González & Fernando Fernández-Rodríguez, 2007. "Model selection via genetic algorithms illustrated with cross-country growth data," Empirical Economics, Springer, vol. 33(2), pages 313-337, September.
    4. King, Gary & Honaker, James & Joseph, Anne & Scheve, Kenneth, 2001. "Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation," American Political Science Review, Cambridge University Press, vol. 95(1), pages 49-69, March.
    5. Hendry, David F., 1995. "Dynamic Econometrics," OUP Catalogue, Oxford University Press, number 9780198283164, Decembrie.
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    Citations

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    Cited by:

    1. Nicoletta Batini, 2019. "Macroeconomic Gains from Reforming the Agri-Food Sector: The Case of France," IMF Working Papers 2019/041, International Monetary Fund.
    2. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(1), pages 1-14, January.
    3. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "Let the data do the talking: Empirical modelling of survey-based expectations by means of genetic programming," IREA Working Papers 201711, University of Barcelona, Research Institute of Applied Economics, revised May 2017.
    4. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Empirical modelling of survey-based expectations for the design of economic indicators in five European regions," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 46(2), pages 205-227, May.
    5. Priyadarshi Dash, 2017. "Predicting Financial Crises: A Study of Asian Economies," Global Business Review, International Management Institute, vol. 18(5), pages 1262-1277, October.
    6. Inès Abdelkafi & Manel Zribi & Rochdi Feki, 2018. "New Classification of Developed and Emerging Countries Based on the Effects of Subprime Crises: Kohonen Map Method," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 9(3), pages 908-927, September.
    7. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "A new approach for the quantification of qualitative measures of economic expectations," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2685-2706, November.
    8. Mustafa Okur & Ali Köse & Özgür Akpinar, 2021. "The Soundness of Financial Institutions In The Fragile Five Countries," International Journal of Business Research and Management (IJBRM), Computer Science Journals (CSC Journals), vol. 12(3), pages 89-102, June.
    9. Paccagnini, Alessia, 2019. "Did financial factors matter during the Great Recession?," Economics Letters, Elsevier, vol. 174(C), pages 26-30.
    10. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "“Tracking economic growth by evolving expectations via genetic programming: A two-step approach”," AQR Working Papers 201801, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2018.
    11. Kauko, Karlo, 2014. "How to foresee banking crises? A survey of the empirical literature," Economic Systems, Elsevier, vol. 38(3), pages 289-308.
    12. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Evolutionary Computation for Macroeconomic Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 833-849, February.

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    More about this item

    Keywords

    Financial crises; Great recession; Genetic algorithms; Model selection;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers

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