Instrumental Variable Estimation of Large Panel Data Models with Common Factors

Abstract

This article introduces the xtivdfreg command in Stata, which implements a general Instrumental Variables (IV) approach for estimating large panel data models with unobserved common factors or interactive effects, as developed by Norkute et al. (2020) and Cui et al. (2020a). The underlying idea of this approach is to project out the common factors from exogenous co-variates using principal components analysis, and run IV regression using de-factored co-variates as instruments. The resulting "IVDF" method is valid for models with homogeneous or heterogeneous slope coefficients, and has several advantages relative to existing popular approaches.

In addition, the xtivdfreg command extends the IVDF approach in two major ways. Firstly, the algorithm accommodates estimation of unbalanced panels. Secondly, the algorithm permits highly
flexible instrumentation strategies.

It is shown that when one imposes zero factors, the xtivdfreg command can replicate the results of the popular ivregress Stata command. Notably, xtivdfreg also permits estimation of the two-way error components panel data model with heterogeneous slope coefficients.

Keywords: Large Panels, Instrumental Variables, Common Factors, Interactive Effects, Cross-Sectional Dependence, Defactoring, Xtivdfreg

JEL Classification: C23, C26, C55, C87