a high‐dimensional parameter in both homogeneous cross‐sectional and unit‐heterogeneous dynamic panel data settings. In … dependent time series and panel data. This method "leaves out the neighbors" when fitting nuisance components, and we …‐dimensional. In heterogeneous panel data settings, we model the unobserved unit heterogeneity as a weakly sparse deviation from …

This paper studies identification in a binary choice panel data model with choice probabilities depending on a lagged …

We study identification in a binary choice panel data model with a single predetermined binary covariate (i.e., a …

This paper develops a Stein-like combined estimator for large heterogeneous panel data models under common structural …

Our confidence set quantifies the statistical uncertainty from data-driven group assignments in grouped panel models …

-sectional, and both static and dynamic panel settings where we focus throughout on inference under sufficient conditions for point … regressors: with cross-section data, we match across individuals while with panel data, we match within individuals over time … presents a generalization of existing approaches. In panel data settings, rates of convergence are shown to exhibit a curse of …

individual enterprises' TFP. We based the estimations on an unbalanced panel dataset containing about 1 329 106 firms yearly …

We propose a new estimator for average causal effects of a binary treatment with panel data in settings with general …

This paper revisits the panel autoregressive model, with a primary emphasis on the unit-root case. We study a class of …