Inference for Large Panel Data with Many Covariates

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.

References in corpus (7)

• High-dimensional graphs and variable selection with the Lasso
• On asymptotically optimal confidence regions and tests for high-dimensional models
• Controlling the false discovery rate via knockoffs
• A Unified Framework for High-Dimensional Analysis of M-Estimators with Decomposable Regularizers
• E-values: Calibration, combination, and applications
• Choosing Between Methods of Combining p-values
• Selective inference with unknown variance via the square-root LASSO