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Optimal forecasting with heterogeneous panels: A Monte Carlo study

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  • Trapani, Lorenzo
  • Urga, Giovanni
Abstract
We contrast the forecasting performance of alternative panel estimators, divided into three main groups: homogeneous, heterogeneous and shrinkage/Bayesian. Via a series of Monte Carlo simulations, the comparison is performed using different levels of heterogeneity and cross sectional dependence, alternative panel structures in terms of T and N and the specification of the dynamics of the error term. To assess the predictive performance, we use traditional measures of forecast accuracy (Theil's U statistics, RMSE and MAE), the Diebold-Mariano test, and Pesaran and Timmerman's statistic on the capability of forecasting turning points. The main finding of our analysis is that when the level of heterogeneity is high, shrinkage/Bayesian estimators are preferred, whilst when there is low or mild heterogeneity, homogeneous estimators have the best forecast accuracy.

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  • Trapani, Lorenzo & Urga, Giovanni, 2009. "Optimal forecasting with heterogeneous panels: A Monte Carlo study," International Journal of Forecasting, Elsevier, vol. 25(3), pages 567-586, July.
    • Handle: RePEc:eee:intfor:v:25:y:2009:i:3:p:567-586
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    2. 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.
    3. 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.
    4. 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.
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    7. 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.
    8. 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.
    9. 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.
    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. Trapani, Lorenzo, 2012. "On the asymptotic t-test for large nonstationary panel models," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3286-3306.
    12. Thomas Jobert & Fatih Karanfil & Anna Tykhonenko, 2014. "Estimating country-specific environmental Kuznets curves from panel data: a Bayesian shrinkage approach," Applied Economics, Taylor & Francis Journals, vol. 46(13), pages 1449-1464, May.
    13. 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.
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    15. 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).

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    More about this item

    Keywords

    Heterogeneity Cross dependence Forecasting Monte Carlo simulations;

    JEL classification:

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

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