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

Tomohiro Ando, Jushan Bai

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)183-211
Number of pages29
JournalEconometric Reviews
Volume37
Issue number3
DOIs
Publication statusPublished - 2018 Mar 16

Keywords

  • Endogeneity
  • factor models
  • heterogeneous coefficients
  • information criterion
  • penalized method
  • smoothly clipped absolute deviation (SCAD)

ASJC Scopus subject areas

  • Economics and Econometrics

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