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Square-root Nuclear Norm Penalized Estimator for Panel Data Models with Approximately Low-rank Unobserved Heterogeneity by Beyhum, Jad; Gautier, Eric is a scholarly article available to read on EtoBox.

What is Square-root Nuclear Norm Penalized Estimator for Panel Data Models with Approximately Low-rank Unobserved Heterogeneity about?

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.

Author

Beyhum, Jad; Gautier, Eric

Published

2019

Language

EN