Multistep Forecast Selection for Panel Data
45 Pages Posted: 17 Jun 2019 Last revised: 18 Jul 2019
Date Written: May 30, 2019
Abstract
We develop a new set of model selection methods for direct multistep forecasting of panel data vector autoregressive processes. Model selection is based on minimizing the estimated multistep quadratic forecast risk among candidate models. In order to attenuate the small sample bias of the least squares estimator, models are fitted using bias-corrected least squares. We provide conditions sufficient for the new selection criteria to be asymptotically efficient in the sense of Shibata (1980, Ann. Statist. 8, 147-164) as n (cross sections) and T (time series) approach infinity. The new criteria outperform alternative selection methods in an empirical application to forecasting metropolitan statistical area population growth in the US.
Keywords: Forecasting, Model Selection, Panel Data, Misspecification, Bias-Correction, Final Prediction Error, Mallows Criterion
JEL Classification: C23, C53
Suggested Citation: Suggested Citation