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A comparison of pre- and post-crisis efficiency of OECD countries: evidence from a model with temporal heterogeneity in time and unobservable individual effect

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  • Roman Matkovskyy

Abstract

The purpose of this article is to estimate and compare shifts in (technical) efficiency across OECD countries, caused by the global financial crises and heterogeneity. Technical efficiency of OECD countries is estimated by applying the panel model with arbitrary temporal heterogeneity in time and factor structures (a model with unobservable individual effects) that fits the stochastic frontier analysis. Because of missing values in observations, the bootstrapping-based algorithm allowing for trends in data across observations within a cross-sectional unit is applied for imputations. The parameters are estimated in a semi-parametric way. The proposed estimation derives sufficient results regardless of any assumption on the temporal pattern of country individual effects and contributes to the development of a tool for better understanding of unobserved factors that drive fluctuations in OECD countries.

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  • Roman Matkovskyy, 2016. "A comparison of pre- and post-crisis efficiency of OECD countries: evidence from a model with temporal heterogeneity in time and unobservable individual effect," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 13(2), pages 135-167, December.
    • Handle: RePEc:liu:liucej:v:13:y:2016:i:2:p:135-167
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    1. Bruno Dallago, 2013. "Financial and real crisis in the Eurozone and vulnerable economies," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 10(3), pages 295-315, December.
    2. Forni, Mario & Lippi, Marco, 2001. "The Generalized Dynamic Factor Model: Representation Theory," Econometric Theory, Cambridge University Press, vol. 17(6), pages 1113-1141, December.
    3. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    4. Camille Chaserant & Sophie Harnay, 2013. "The regulation of quality in the market for legal services: Taking the heterogeneity of legal services seriously," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-01271355, HAL.
    5. Fried, Harold O. & Lovell, C. A. Knox & Schmidt, Shelton S. (ed.), 2008. "The Measurement of Productive Efficiency and Productivity Growth," OUP Catalogue, Oxford University Press, number 9780195183528, Decembrie.
    6. Seung C. Ahn & Alex R. Horenstein, 2013. "Eigenvalue Ratio Test for the Number of Factors," Econometrica, Econometric Society, vol. 81(3), pages 1203-1227, May.
    7. Roman Matkovskyy, 2016. "Arbitrary temporal heterogeneity in time of European countries panel model," Economics Bulletin, AccessEcon, vol. 36(1), pages 576-587.
    8. Kobi Abayomi & Andrew Gelman & Marc Levy, 2008. "Diagnostics for multivariate imputations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(3), pages 273-291, June.
    9. Uwe Vollmer & Ralf Bebenroth, 2012. "The Financial Crisis in Japan: Causes and Policy Reactions by the Bank of Japan," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 9(1), pages 51-77, April.
    10. Kneip, Alois & Sickles, Robin C. & Song, Wonho, 2012. "A New Panel Data Treatment For Heterogeneity In Time Trends," Econometric Theory, Cambridge University Press, vol. 28(3), pages 590-628, June.
    11. Hallin, Marc & Liska, Roman, 2007. "Determining the Number of Factors in the General Dynamic Factor Model," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 603-617, June.
    12. 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.
    13. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    14. Seung Ahn & Young Lee & Peter Schmidt, 2007. "Stochastic frontier models with multiple time-varying individual effects," Journal of Productivity Analysis, Springer, vol. 27(1), pages 1-12, February.
    15. Forsund, Finn R. & Lovell, C. A. Knox & Schmidt, Peter, 1980. "A survey of frontier production functions and of their relationship to efficiency measurement," Journal of Econometrics, Elsevier, vol. 13(1), pages 5-25, May.
    16. Roberto Tamborini, 2013. "The new fiscal rules for the EMU. Threats from heterogeneity and interdependence," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 10(3), pages 415-436, December.
    17. Adams, Robert M & Berger, Allen N & Sickles, Robin C, 1999. "Semiparametric Approaches to Stochastic Panel Frontiers with Applications in the Banking Industry," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(3), pages 349-358, July.
    18. Laurens Cherchye & Wim Moesen & Tom Van Puyenbroeck, 2004. "Legitimately Diverse, yet Comparable: On Synthesizing Social Inclusion Performance in the EU," Journal of Common Market Studies, Wiley Blackwell, vol. 42(5), pages 919-955, December.
    19. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-374, October.
    20. Despotis, D.K., 2005. "Measuring human development via data envelopment analysis: the case of Asia and the Pacific," Omega, Elsevier, vol. 33(5), pages 385-390, October.
    21. Honaker, James & King, Gary & Blackwell, Matthew, 2011. "Amelia II: A Program for Missing Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i07).
    22. Alexei Onatski, 2010. "Determining the Number of Factors from Empirical Distribution of Eigenvalues," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 1004-1016, November.
    23. Bai, Jushan, 2004. "Estimating cross-section common stochastic trends in nonstationary panel data," Journal of Econometrics, Elsevier, vol. 122(1), pages 137-183, September.
    24. Camille Chaserant & Sophie Harnay, 2013. "The regulation of quality in the market for legal services: Taking the heterogeneity of legal services seriously," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 10(2), pages 267-291, August.
    25. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, July.
    26. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    27. Alessi, Lucia & Barigozzi, Matteo & Capasso, Marco, 2010. "Improved penalization for determining the number of factors in approximate factor models," Statistics & Probability Letters, Elsevier, vol. 80(23-24), pages 1806-1813, December.
    28. Bada, Oualid & Liebl, Dominik, 2014. "phtt: Panel Data Analysis with Heterogeneous Time Trends in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 59(i06).
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    More about this item

    Keywords

    Efficiency; Stochastic Distance Frontier; Heterogeneity in Time; Unobserved Factors; Principal Component Analysis; Comparative Economics;
    All these keywords.

    JEL classification:

    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • F15 - International Economics - - Trade - - - Economic Integration
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • O52 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Europe

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