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

Greenaway-McGrevy, Ryan, Multistep Forecast Selection for Panel Data (May 30, 2019). Available at SSRN: https://ssrn.com/abstract=3398621 or http://dx.doi.org/10.2139/ssrn.3398621

Ryan Greenaway-McGrevy (Contact Author)

University of Auckland Business School ( email )

12 Grafton Rd
Private Bag 92019
Auckland, 1010
New Zealand

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