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Optimal forecasting with heterogeneous panels: a Monte Carlo study

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  • 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 estimators, divided into three main groups (homogeneous, heterogeneous and shrinkage/Bayesian). The comparison is done using different levels 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 finding of our analysis is that in presence of heterogeneous panels the Bayesian procedures have systematically the best predictive power independently of the model's features.

Suggested Citation

  • Lorenzo Trapani & Giovanni Urga, 2006. "Optimal forecasting with heterogeneous panels: a Monte Carlo study," Working Papers 0616, Department of Management, Information and Production Engineering, University of Bergamo.
    • Handle: RePEc:brh:wpaper:0616
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    2. Ken Imanak Sagynbekov, 2014. "A tale of six states: How similar are the Gulf Cooperation Council countries?," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 23(4), pages 476-490, June.
    3. Anna Tykhonenko & Donnat Grégory, 2022. "Debt Relief: The Day After, Financing Low-Income Countries," Post-Print hal-04298772, HAL.
    4. Thomas Jobert & Fatih Karanfil & Anna Tykhonenko, 2012. "Trade and Environment: Further Empirical Evidence from Heterogeneous Panels Using Aggregate Data," GREDEG Working Papers 2012-15, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    5. David Schröder & Andrew Yim, 2018. "Industry Effects in Firm and Segment Profitability Forecasting," Contemporary Accounting Research, John Wiley & Sons, vol. 35(4), pages 2106-2130, December.
    6. Baltagi, Badi H., 2013. "Panel Data Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 995-1024, Elsevier.
    7. Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2014. "Estimating and Forecasting with a Dynamic Spatial Panel Data Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 112-138, February.
    8. Thomas Jobert & Alexandru Monahov & Anna Tykhonenko, 2014. "Domestic Credit in Times of Supervision: An Empirical Investigation of European Countries," GREDEG Working Papers 2014-30, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    9. Massimiliano Mazzanti & Antonio Musolesi, 2013. "The heterogeneity of carbon Kuznets curves for advanced countries: comparing homogeneous, heterogeneous and shrinkage/Bayesian estimators," Applied Economics, Taylor & Francis Journals, vol. 45(27), pages 3827-3842, September.
    10. 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.
    11. 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.
    12. Morales-Arias, Leonardo & Dross, Alexander, 2010. "Adaptive forecasting of exchange rates with panel data," Kiel Working Papers 1656, Kiel Institute for the World Economy (IfW Kiel).
    13. Trapani, Lorenzo, 2012. "On the asymptotic t-test for large nonstationary panel models," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3286-3306.
    14. Akgun, Oguzhan & Pirotte, Alain & Urga, Giovanni, 2020. "Forecasting using heterogeneous panels with cross-sectional dependence," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1211-1227.
    15. Ernesto Aguayo-T鬬ez & Jos頍art󹑺-Navarro, 2013. "Internal and international migration in Mexico: 1995--2000," Applied Economics, Taylor & Francis Journals, vol. 45(13), pages 1647-1661, May.

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    More about this item

    Keywords

    Panel data; homogeneous; heterogeneous and shrinkage estimators; forecasting; cross dependence; Monte Carlo simulations;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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