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Optimal forecasting with heterogeneous panels: a Monte Carlo study

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Author Info

  • Lorenzo Trapani
  • Giovanni Urga

Abstract

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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Bibliographic Info

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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Keywords: Panel data; homogeneous; heterogeneous and shrinkage estimators; forecasting; cross dependence; Monte Carlo simulations.;

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References

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  1. Badi H. Baltagi & Georges Bresson & James M. Griffin & Alain Pirotte, 2002. "Homogeneous, heterogeneous or shrinkage estimators? Some empirical evidence from French regional gasoline consumption," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 A6-4, International Conferences on Panel Data.
  2. Arellano, M. & Honore, B., 2000. "Panel Data Models: Some Recent Developments," Papers 0016, Centro de Estudios Monetarios Y Financieros-.
  3. Pesaran, M.H., 2003. "A Simple Panel Unit Root Test in the Presence of Cross Section Dependence," Cambridge Working Papers in Economics 0346, Faculty of Economics, University of Cambridge.
  4. Christoffersen, Peter F & Diebold, Francis X, 1996. "Further Results on Forecasting and Model Selection under Asymmetric Loss," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 561-71, Sept.-Oct.
  5. 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.
  6. Herbert Brücker & Boriss Siliverstovs, 2006. "On the estimation and forecasting of international migration: how relevant is heterogeneity across countries?," Empirical Economics, Springer, vol. 31(3), pages 735-754, September.
  7. Pesaran, M.H. & Timmermann, A., 1990. "A Simple Non-Parametric Test Of Predictive Performance," Papers 29, California Los Angeles - Applied Econometrics.
  8. Leitch, Gordon & Tanner, J Ernest, 1991. "Economic Forecast Evaluation: Profits versus the Conventional Error Measures," American Economic Review, American Economic Association, vol. 81(3), pages 580-90, June.
  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. 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.
  11. Francis X. Diebold & Jose A. Lopez, 1995. "Forecast evaluation and combination," Research Paper 9525, Federal Reserve Bank of New York.
  12. 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.
  13. 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.
  14. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
  15. Arellano, Manuel, 2003. "Panel Data Econometrics," OUP Catalogue, Oxford University Press, number 9780199245291.
  16. Sevestre, P. & Trognon, A., 1985. "A note on autoregressive error components models," Journal of Econometrics, Elsevier, vol. 28(2), pages 231-245, May.
  17. Pesaran, M. Hashem & Smith, Ron, 1995. "Estimating long-run relationships from dynamic heterogeneous panels," Journal of Econometrics, Elsevier, vol. 68(1), pages 79-113, July.
  18. 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.
  19. 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.
  20. 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.
  21. Hsiao,Cheng, 2003. "Analysis of Panel Data," Cambridge Books, Cambridge University Press, number 9780521818551, November.
  22. Breusch, Trevor S., 1987. "Maximum likelihood estimation of random effects models," Journal of Econometrics, Elsevier, vol. 36(3), pages 383-389, November.
  23. Arellano, Manuel & Bond, Stephen, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Wiley Blackwell, vol. 58(2), pages 277-97, April.
  24. Anderson, T. W. & Hsiao, Cheng, 1982. "Formulation and estimation of dynamic models using panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 47-82, January.
  25. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-26, November.
  26. Ciaran Driver & Giovanni Urga, 2004. "Transforming Qualitative Survey Data: Performance Comparisons for the UK," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(1), pages 71-89, 02.
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Cited by:
  1. Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2012. "Estimating and Forecasting With A Dynamic Spatial Panel Data Model," Center for Policy Research Working Papers 149, Center for Policy Research, Maxwell School, Syracuse University.
  2. Leonardo Morales-Arias & Alexander Dross, 2010. "Adaptive Forecasting of Exchange Rates with Panel Data," Research Paper Series 285, Quantitative Finance Research Centre, University of Technology, Sydney.
  3. Morales-Arias, Leonardo & Moura, Guilherme V., 2013. "Adaptive forecasting of exchange rates with panel data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 493-509.
  4. Reibling, Nadine, 2013. "The international performance of healthcare systems in population health: Capabilities of pooled cross-sectional time series methods," Health Policy, Elsevier, vol. 112(1), pages 122-132.
  5. Trapani, Lorenzo, 2012. "On the asymptotic t-test for large nonstationary panel models," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3286-3306.
  6. Massimiliano Mazzanti & Antonio Musolesi, 2012. "The heterogeneity of Carbon Kuznets Curves for advanced countries. Comparing homogeneous, heterogeneous and shrinkage/Bayesian estimators," Working Papers 201206, University of Ferrara, Department of Economics.
  7. Leonardo Morales-Arias & Alexander Dross, 2010. "Adaptive Forecasting of Exchange Rates with Panel Data," Kiel Working Papers 1656, Kiel Institute for the World Economy.

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