Nuclear Norm Regularized Estimation of Panel Regression Models
AbstractIn this paper we investigate panel regression models with interactive fixed effects.
We propose two new estimation methods that are based on minimizing convex objective functions. The first estimation method minimizes the sum of squared residuals
with a nuclear (trace) norm regularization. The second estimation method minimizes
the nuclear norm of the residuals. First, we establish the consistency of the two estimators, and then we show how to use these two estimators as a preliminary estimator
and to construct an estimator that is asymptotically equivalent to the QMLE in Bai
(2009) and Moon and Weidner (2017). For this, we propose an iteration procedure and
derive its asymptotic properties.