This article introduces a straightforward sieve-based approach for estimation and inference of regression parameters in panel data models with interactive fixed effects. The method’s key assumption is
This paper presents new estimation algorithms for three types of dynamic panel data models with latent variables: factor models, discrete choice models, and persistent-transitory quantile processes. T
In this article, we consider estimation of common structural breaks in panel data models with unobservable interactive fixed effects. We introduce a penalized principal component (PPC) estimation proc
We develop a procedure to identify latent group structures in linear panel data models that exploits a grouping in the error variances of cross-sectional units. To accommodate such grouping, we introd
