We present a new estimator for causal effects with panel data that builds on insights behind
the widely used difference-in-differences and synthetic control methods. Relative to these …

Large factor models use a few latent factors to characterize the co-movement of economic
variables in a high-dimensional data set. High dimensionality brings challenges as well as …

This is the first paper in a series of two that synthesizes, compares, and extends methods for
causal inference with longitudinal panel data in a structural equation modeling (SEM) …

In line with the pursuit of clean and affordable energy, our study contributes to the United
Nations Sustainable Development Goals (UNSDGs-7 and-13) and the global fight against …

The paper deals with the estimation of a high dimensional covariance with a conditional
sparsity structure and fast diverging eigenvalues. By assuming a sparse error covariance …

This article describes sfcross and sfpanel, two new Stata commands for the estimation of
cross-sectional and panel-data stochastic frontier models. sfcross extends the capabilities of …

This paper proposes simple tests of error cross-sectional dependence which are applicable
to a variety of panel data models, including stationary and unit root dynamic heterogeneous …

This paper presents a new approach to estimation and inference in panel data models with
a general multifactor error structure. The unobserved factors and the individual‐specific …

In this paper, we investigate the use of interactive effect or linear factor models in regional
policy evaluation. We contrast treatment effect estimates obtained using Bai (2009) with …

This paper considers large N and large T panel data models with unobservable multiple
interactive effects, which are correlated with the regressors. In earnings studies, for example …