Optimal forecasting with heterogeneous panels: a Monte Carlo study

Lorenzo Trapani () and Giovanni Urga

No 616, Working Papers from Department of Management, Information and Production Engineering, University of Bergamo

Abstract: This paper reports the results of a series of Monte Carlo exercises to contrast the forecasting performance of several panel data estimators, divided into three main groups (homogeneous, heterogeneous and shrinkage/Bayesian). The comparison is done using different levels of heterogeneity, alternative panel structures in terms of T and N and using various error dynamics speci.cations. We also consider the presence of various degrees of cross sectional dependence among units. To assess the predictive performance, we use traditional measures of forecast accuracy (Theil's U statistics, RMSE and MAE), the Diebold and Mariano's (1995) test, and the Pesaran and Timmerman's (1992) statistics on the capability of forecasting turning points. The main finding of our analysis is that in presence of heterogeneous panels the Bayesian procedures have systematically the best predictive power independently of the model's features.

Keywords: Panel data; homogeneous, heterogeneous and shrinkage estimators; forecasting; cross dependence; Monte Carlo simulations (search for similar items in EconPapers)
JEL-codes: C23 (search for similar items in EconPapers)
Date: 2006
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10446/427 (application/pdf)

Related works:
Journal Article: Optimal forecasting with heterogeneous panels: A Monte Carlo study (2009)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:brh:wpaper:0616

Access Statistics for this paper

More papers in Working Papers from Department of Management, Information and Production Engineering, University of Bergamo Contact information at EDIRC.
Bibliographic data for series maintained by University of Bergamo Library ().