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Selecting the regularization parameters in high-dimensional panel data models: Consistency and efficiency

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  • Tomohiro Ando
  • Jushan Bai

Abstract

This article considers panel data models in the presence of a large number of potential predictors and unobservable common factors. The model is estimated by the regularization method together with the principal components procedure. We propose a panel information criterion for selecting the regularization parameter and the number of common factors under a diverging number of predictors. Under the correct model specification, we show that the proposed criterion consistently identifies the true model. If the model is instead misspecified, the proposed criterion achieves asymptotically efficient model selection. Simulation results confirm these theoretical arguments.

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  • Tomohiro Ando & Jushan Bai, 2018. "Selecting the regularization parameters in high-dimensional panel data models: Consistency and efficiency," Econometric Reviews, Taylor & Francis Journals, vol. 37(3), pages 183-211, March.
    • Handle: RePEc:taf:emetrv:v:37:y:2018:i:3:p:183-211
    DOI: 10.1080/07474938.2015.1092822
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    Cited by:

    1. Tomohiro Ando & Jushan Bai, 2020. "Quantile Co-Movement in Financial Markets: A Panel Quantile Model With Unobserved Heterogeneity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 266-279, January.

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