Penalized (or regularized) regression, as represented by lasso and its variants, has become
a standard technique for analyzing high-dimensional data when the number of variables …

This paper establishes non-asymptotic oracle inequalities for the prediction error and
estimation accuracy of the LASSO in stationary vector autoregressive models. These …

In this paper we consider estimation and inference of common breaks in panel data models
via adaptive group fused Lasso. We consider two approaches—penalized least squares …

We consider the problem of determining the number of factors and selecting the proper
regressors in linear dynamic panel data models with interactive fixed effects. Based on the …

This study examines high-dimensional forecasting and variable selection via folded-
concave penalized regressions. The penalized regression approach leads to sparse …

This article reviews recent endeavors to incorporate big data and machine learning
techniques into energy and environmental economics research. We find that novel datasets …

In this paper we consider the conservative Lasso which we argue penalizes more correctly
than the Lasso and show how it may be desparsified in the sense of van de Geer et …

In this paper, we consider the problem of determining the number of structural changes in
multiple linear regression models via group fused Lasso. We show that with probability …

In this paper, we propose a semiparametric varying-coefficient categorical panel data model
in which covariates (variables affecting the coefficients) are purely categorical. This model …

We establish oracle inequalities for a version of the Lasso in high-dimensional fixed effects
dynamic panel data models. The inequalities are valid for the coefficients of the dynamic and …