Homogeneity and Sparsity Analysis for High-Dimensional Panel Data Models

In this article, we are interested in detecting latent group structures and significant covariates a high-dimensional panel data model with both individual time fixed effects. The slope coefficients of the assumed to be subject dependent, there exist where homogeneous within groups heterogeneous between groups. We develop penalized estimator for recovering sparsity patterns simultaneously. propose new algorithm optimize objective function. Furthermore, strategy reduce computational complexity by pruning penalty terms function, which also improves accuracy structure detection. proposed can recover consistently large samples. finite sample performance is evaluated through Monte Carlo studies illustrated real dataset.