This article introduces lassopack, a suite of programs for regularized regression in Stata. lassopack implements lasso …, square-root lasso, elastic net, ridge regression, adaptive lasso and post-estimation OLS. The methods are suitable for the … (implemented in lasso2), K-fold cross-validation and h-step ahead rolling cross-validation for cross-section, panel and time …

sparsity of the spatial weights matrix. The proposed estimation methodology exploits the Lasso estimator and mimics two … larger than the number of observations. We derive convergence rates for the two-step Lasso estimator. Our Monte Carlo …

sparsity of the spatial weights matrix. The proposed estimation methodology exploits the Lasso estimator and mimics two … larger than the number of observations. We derive convergence rates for the two-step Lasso estimator. Our Monte Carlo …

This article introduces lassopack, a suite of programs for regularized regression in Stata. lassopack implements lasso …, square-root lasso, elastic net, ridge regression, adaptive lasso and post-estimation OLS. The methods are suitable for the … (implemented in lasso2), K-fold cross-validation and h-step ahead rolling cross-validation for cross-section, panel and time …

approximate sparsity of the spatial weights matrix. The proposed estimation methodology exploits the Lasso estimator and mimics … variables is larger than the number of observations. We derive convergence rates for the two-step Lasso estimator. Our Monte …