Optimal forecasting with heterogeneous panels: A Monte Carlo study
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.
Year of publication: |
2009
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Authors: | Trapani, Lorenzo ; Urga, Giovanni |
Published in: |
International Journal of Forecasting. - Elsevier, ISSN 0169-2070. - Vol. 25.2009, 3, p. 567-586
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Publisher: |
Elsevier |
Keywords: | Heterogeneity Cross dependence Forecasting Monte Carlo simulations |
Saved in:
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