Varying-Coefficient Panel Data Models with Partially Observed Factor Structure

Abstract

In this paper, we study a varying-coefficient panel data model with nonstationarity, wherein a factor structure is adopted to capture different effects of time invariant variables over time. The methodology employed in this paper fills a gap of dealing with the mixed I(1)/I(0) regressors and factors in the literature. For the purpose of comparison, we consider both of the scenarios where the factors are observable and unobservable, respectively. We then propose semiparametric estimators and establish the corresponding theory. We evaluate the finite-sample performance of proposed estimation theory through extensive Monte Carlo simulations. In an empirical study, we use our newly proposed model and method to study the returns to scale of large commercial banks in the U.S.. Some overlooked modelling issues in the literature of production econometrics are addressed.