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Forecast combination for VARs in large N and T panels

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  • Greenaway-McGrevy, Ryan

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 Cp 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.

Suggested Citation

  • Greenaway-McGrevy, Ryan, 2022. "Forecast combination for VARs in large N and T panels," International Journal of Forecasting, Elsevier, vol. 38(1), pages 142-164.
  • Handle: RePEc:eee:intfor:v:38:y:2022:i:1:p:142-164
    DOI: 10.1016/j.ijforecast.2021.04.006
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