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Optimal forecasting with heterogeneous panels: a Monte Carlo study

  • Lorenzo Trapani
  • Giovanni Urga

This paper reports the results of a series of Monte Carlo exercises to contrast the forecasting performance of several panel data esti- mators, divided into three main groups (homogeneous, heterogeneous and shrinkage/Bayesian). The comparison is done using di¤erent lev- els of heterogeneity, alternative panel structures in terms of T and N and using various error dynamics speci.cations. We also consider the presence of various degrees of cross sectional dependence among units. To assess the predictive performance, we use traditional measures of forecast accuracy (Theil.s U statistics, RMSE and MAE), the Diebold and Mariano.s (1995) test, and the Pesaran and Timmerman.s (1992) statistics on the capability of forecasting turning points. The main .nding of our analysis is that in presence of heterogeneous panels the Bayesian procedures have systematically the best predictive power in- dependently of the model.s features.

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Paper provided by Department of Economics and Technology Management, University of Bergamo in its series Working Papers with number 0616.

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Date of creation: 2006
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Handle: RePEc:brh:wpaper:0616
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  7. Baltagi, Badi H. & Li, Qi, 1992. "A Note on the Estimation of Simultaneous Equations with Error Components," Econometric Theory, Cambridge University Press, vol. 8(01), pages 113-119, March.
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  9. Badi H. Baltagi & Georges Bresson & Alain Pirotte, 2004. "Tobin q: Forecast performance for hierarchical Bayes, shrinkage, heterogeneous and homogeneous panel data estimators," Empirical Economics, Springer, vol. 29(1), pages 107-113, January.
  10. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
  11. Badi H. Baltagi & Georges Bresson & James M. Griffin & Alain Pirotte, 2003. "Homogeneous, heterogeneous or shrinkage estimators? Some empirical evidence from French regional gasoline consumption," Empirical Economics, Springer, vol. 28(4), pages 795-811, November.
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  13. Pesaran, M.H. & Smith, R., 1992. "Estimating Long-Run Relationships From Dynamic Heterogeneous Panels," Cambridge Working Papers in Economics 9215, Faculty of Economics, University of Cambridge.
  14. Balestra, Pietro & Varadharajan-Krishnakumar, Jayalakshmi, 1987. "Full Information Estimations of a System of Simultaneous Equations with Error Component Structure," Econometric Theory, Cambridge University Press, vol. 3(02), pages 223-246, April.
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  16. Arellano, Manuel, 2003. "Panel Data Econometrics," OUP Catalogue, Oxford University Press, number 9780199245291, March.
  17. M. Hashem Pesaran, 2007. "A simple panel unit root test in the presence of cross-section dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(2), pages 265-312.
  18. Francis X. Diebold & Jose A. Lopez, 1995. "Forecast evaluation and combination," Research Paper 9525, Federal Reserve Bank of New York.
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  20. Keane, Michael P & Runkle, David E, 1992. "On the Estimation of Panel-Data Models with Serial Correlation When Instruments Are Not Strictly Exogenous," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(1), pages 1-9, January.
  21. Baltagi, Badi H. & Bresson, Georges & Pirotte, Alain, 2002. "Comparison of forecast performance for homogeneous, heterogeneous and shrinkage estimators: Some empirical evidence from US electricity and natural-gas consumption," Economics Letters, Elsevier, vol. 76(3), pages 375-382, August.
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  24. Keane, Michael P & Runkle, David E, 1992. "On the Estimation of Panel-Data Models with Serial Correlation When Instruments Are Not Strictly Exogenous: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(1), pages 26-29, January.
  25. Baltagi, Badi H. & Griffin, James M., 1997. "Pooled estimators vs. their heterogeneous counterparts in the context of dynamic demand for gasoline," Journal of Econometrics, Elsevier, vol. 77(2), pages 303-327, April.
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