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ASYMPTOTICALLY EFFICIENT MODEL SELECTION FOR PANEL DATA FORECASTING

  • Ryan Greenaway-McGrevy (a1)
Abstract

This article develops new model selection methods for forecasting panel data using a set of least squares (LS) vector autoregressions. Model selection is based on minimizing the estimated quadratic forecast risk among candidate models. We provide conditions under which the selection criterion is asymptotically efficient in the sense of Shibata (1980) as n (cross sections) and T (time series) approach infinity. Relative to extant selection criteria, this criterion places a heavier penalty on model dimensionality in order to account for the effects of parameterized forms of cross sectional heterogeneity (such as fixed effects) on forecast loss. We also extend the analysis to bias-corrected least squares, showing that significant reductions in forecast risk can be achieved.

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Corresponding author
*Address correspondence to Ryan Greenaway-McGrevy, Department of Economics, The University of Auckland, Auckland, New Zealand; e-mail: r.mcgrevy@auckland.ac.nz.
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The author thanks Yong Bao, Peter C.B. Phillips, Donggyu Sul, and seminar participants at the University of Auckland, the 23rd NZESG meeting, and the 22nd Midwest Econometrics Group meeting for their comments. This work was supported in part by the Marsden Fund Council from Government funding, administered by the Royal Society of New Zealand under grant No. 16-UOA-239.

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Econometric Theory
  • ISSN: 0266-4666
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