Optimal forecasting with heterogeneous panels: A Monte Carlo study

Lorenzo Trapani () and Giovanni Urga

International Journal of Forecasting, 2009, vol. 25, issue 3, 567-586

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.

Keywords: Heterogeneity; Cross; dependence; Forecasting; Monte; Carlo; simulations (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (20)

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Working Paper: Optimal forecasting with heterogeneous panels: a Monte Carlo study (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:25:y:2009:i:3:p:567-586

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