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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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Bibliographic Info

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 25 (2009)
Issue (Month): 3 (July)
Pages: 567-586

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Handle: RePEc:eee:intfor:v:25:y:2009:i:3:p:567-586

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Web page: http://www.elsevier.com/locate/ijforecast

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Keywords: Heterogeneity Cross dependence Forecasting Monte Carlo simulations;

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Citations

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Cited by:
  1. Ernesto Aguayo-Téllez & José Martínez-Navarro, 2013. "Internal and international migration in Mexico: 1995--2000," Applied Economics, Taylor & Francis Journals, vol. 45(13), pages 1647-1661, May.
  2. Trapani, Lorenzo, 2012. "On the asymptotic t-test for large nonstationary panel models," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3286-3306.
  3. Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2011. "Estimating and Forecasting with a Dynamic Spatial Panel Data Model," SERC Discussion Papers 0095, Spatial Economics Research Centre, LSE.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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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