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Optimal forecasting with heterogeneous panels: A Monte Carlo study

Trapani, L. & Urga, G. (2009). Optimal forecasting with heterogeneous panels: A Monte Carlo study. International Journal of Forecasting, 25(3), pp. 567-586. doi: 10.1016/j.ijforecast.2009.02.001


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.

Publication Type: Article
Additional Information: NOTICE: this is the author’s version of a work that was accepted for publication in International Journal of Forecasting. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Forecasting, Volume 25, Issue 3, July–September 2009, Pages 567–586,
Publisher Keywords: Heterogeneity; Cross dependence; Forecasting; Monte Carlo simulations
Subjects: H Social Sciences > HG Finance
Departments: Bayes Business School > Finance
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[thumbnail of trapani urga ijf 20 feb 2007 - 3rd revision -15 december 2008.pdf]
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