In this paper, we develop spectral and post-spectral estimators for grouped panel data
models. Both estimators are consistent in the asymptotics where the number of observations …

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 …

This paper considers a linear panel model with interactive fixed effects and unobserved
individual and time heterogeneities that are captured by some latent group structures and an …

This study provides new mechanisms for identifying and estimating explosive bubbles in
mixed‐root panel autoregressions with a latent group structure. A postclustering approach is …

A recent literature in econometrics models unobserved cross-sectional heterogeneity in
panel data by assigning each cross-sectional unit a one-dimensional, discrete latent type …

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 …

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 …

Consider a panel data setting where repeated observations on individuals are available.
Often it is reasonable to assume that there exist groups of individuals that share similar …

This paper studies the estimation of multi-dimensional heterogeneous parameters in a
nonlinear panel data model with endogeneity. These heterogeneous parameters are …

Panel data contains information that describes the economic characteristics of various units
(individuals, firms, countries) over multiple periods. The growing availability of large panel …