Square-root nuclear norm penalized estimator for panel data models with approximately low-rank unobserved Heterogeneity

Abstract

This paper considers a nuclear norm penalized estimator for panel data models with interactive effects. The low-rank interactive effects can be an approximate model and the rank of the best approximation unknown and grow with sample size. The estimator is solution of a well-structured convex optimization problem and can be solved in polynomial-time. We derive rates of convergence, study the low-rank properties of the estimator, estimation of the rank and of annihilator matrices when the number of time periods grows with the sample size. Two-stage estimators can be asymptotically normal. None of the procedures require knowledge of the variance of the errors.

Reference

Jad Beyhum, and Eric Gautier, “Square-root nuclear norm penalized estimator for panel data models with approximately low-rank unobserved Heterogeneity”, TSE Working Paper, n. 19-1008, April 2019.