ASYMPTOTICALLY EFFICIENT MODEL SELECTION FOR PANEL DATA FORECASTING

Author:

Greenaway-McGrevy Ryan

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.

Publisher

Cambridge University Press (CUP)

Subject

Economics and Econometrics,Social Sciences (miscellaneous)

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Nowcasting from cross‐sectionally dependent panels;Journal of Applied Econometrics;2023-05-23

2. Panel data nowcasting;Econometric Reviews;2022-02-03

3. Forecast combination for VARs in large N and T panels;International Journal of Forecasting;2022-01

4. Persistent shocks and incomplete regional adjustment: a model-averaging approach;Regional Studies;2021-08-13

5. Panel VAR models with interactive fixed effects;The Econometrics Journal;2020-07-17

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