In panel data analysis, it is important to identify subgroups of units with heterogeneous
parameters. This can not only increase the model flexibility but also produce more efficient …

We propose a procedure to identify latent group structures in nonlinear panel data models
where some regression coefficients are heterogeneous across groups but homogeneous …

This paper considers the identification of latent group structures in spatial dynamic panels.
We follow Lee and Yu (2010) and consider a rich spatial dynamic panel data (SDPD) model …

The present special issue features a collection of papers presented at the 2017 International
Panel Data Conference, hosted by the University of Macedonia in Thessaloniki, Greece. The …

This paper develops two fully modified OLS (FM-OLS) estimators for systems of seemingly
unrelated cointegrating polynomial regressions, ie, systems of regressions that include …

In non-experimental longitudinal studies, researchers often estimate causal effects
assuming time-constant unobserved heterogeneity or linear-in-parameters conditional …

In high-dimensional data modeling, variable selection methods have been a popular choice
to improve the prediction accuracy by effectively selecting the subset of informative …

Panel data analysis is an important topic in statistics and econometrics. Traditionally, in
panel data analysis, all individuals are assumed to share the same unknown parameters, eg …

In this article, we are interested in detecting latent group structures and significant covariates
in a high-dimensional panel data model with both individual and time fixed effects. The …

Factor modeling is an essential tool for exploring intrinsic dependence structures in financial
and economic studies through the construction of common latent variables, including the …