This paper is concerned with high-dimensional panel data models where the number of regressors can be much larger than the sample size. Under the assumption that the true parameter vector is sparse we establish finite sample upper bounds on the estimation error of the Lasso under two different...

In this paper, we compare two different variable selection approaches for linear regression models: Autometrics (automatic general-to-specific selection) and LASSO (ℓ1-norm regularization). In a simulation study, we show the performance of the methods considering the predictive power (forecast...

In this paper, we compare two different variable selection approaches for linear regression models: Autometrics (automatic general-to-specific selection) and LASSO (?1-norm regularization). In a simulation study, we show the performance of the methods considering the predictive power (forecast...

We study the asymptotic properties of the Adaptive LASSO (adaLASSO) in sparse, high-dimensional, linear time-series models. We assume both the number of covariates in the model and candidate variables can increase with the number of observations and the number of candidate variables is,...