popular approaches in this research field is given by Lasso-type methods. An alternative approach is based on information … criteria. In contrast to the Lasso, these methods also work well in the case of highly correlated predictors. However, this …

random forests, lasso, ridge, deep neural nets, boosted trees, as well as various hybrids and aggregates of these methods (e ….g. a hybrid of a random forest and lasso). We illustrate the application of the general theory through application to the …

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 …

regular case. We propose to estimate such models by the adaptive lasso maximum likelihood and propose an information criterion …

We use lasso methods to shrink, select and estimate the network linking the publicly-traded subset of the world's top …

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 …