Forecast Combination for Panel Data Vectorautoregressions

46 Pages Posted: 25 Jun 2019

Date Written: June 20, 2019

Abstract

We propose a new forecast combination method for panel data vector autoregressions that permit limited forms of parameterized heterogeneity (including fixed effects or incidental trends). Models are fitted using bias-corrected least squares in order to attenuate the effects of small sample bias of forecast loss. We begin by constructing a general estimator of the quadratic forecast risk of the averaged model that is asymptotically unbiased as both n (cross sections) and T (time series) grow large. Armed with this result, we propose a specific weighting mechanism, in which weights are chosen to minimize the estimated quadratic risk of the averaged forecast error. The objective function in this minimization problem is a version of the Mallows C_{p} criterion modified for application to the panel data setting. The forecast combination method performs well in Monte Carlo simulations and pseudo out-of-sample forecasting applications.

Keywords: Forecast Combination, Model Averaging, Panel Data, Mallows Criterion, Bias-Correction

JEL Classification: C23, C53

Suggested Citation

Greenaway-McGrevy, Ryan, Forecast Combination for Panel Data Vectorautoregressions (June 20, 2019). Available at SSRN: https://ssrn.com/abstract=3407008 or http://dx.doi.org/10.2139/ssrn.3407008

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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