Inference for Large Panel Data with Many Covariates

65 Pages Posted: 31 Dec 2022 Last revised: 6 Mar 2023

See all articles by Markus Pelger

Markus Pelger

Stanford University - Department of Management Science & Engineering

Jiacheng Zou

Stanford University - Department of Management Science & Engineering

Date Written: December 31, 2022

Abstract

This paper proposes a novel testing procedure for selecting a sparse set of covariates that explains a large dimensional panel. Our selection method provides correct false detection control while having higher power than existing approaches. We develop the inferential theory for large panels with many covariates by combining post-selection inference with a novel multiple testing adjustment. Our data-driven hypotheses are conditional on the sparse covariate selection. We control for family-wise error rates for covariate discovery for large cross-sections. As an easy-to-use and practically relevant procedure, we propose Panel-PoSI, which combines the data-driven adjustment for panel multiple testing with valid post-selection p-values of a generalized LASSO, that allows us to incorporate priors. In an empirical study, we select a small number of asset pricing factors that explain a large cross-section of investment strategies. Our method dominates the benchmarks out-of-sample due to its better size and power.

Keywords: panel data, high-dimensional data, LASSO, number of covariates, post-selection inference, multiple testing, adaptive hypothesis, step-down procedures, factor model

JEL Classification: C33, C38, C52, C55, G12

Suggested Citation

Pelger, Markus and Zou, Jiacheng, Inference for Large Panel Data with Many Covariates (December 31, 2022). Available at SSRN: https://ssrn.com/abstract=4315891 or http://dx.doi.org/10.2139/ssrn.4315891

Markus Pelger (Contact Author)

Stanford University - Department of Management Science & Engineering ( email )

473 Via Ortega
Stanford, CA 94305-9025
United States

Jiacheng Zou

Stanford University - Department of Management Science & Engineering ( email )

473 Via Ortega
Stanford, CA 94305-9025
United States

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