Explanatory variables in a predictive regression typically exhibit low signal strength and
various degrees of persistence. Variable selection in such a context is of great importance …

Abstract We propose a Least Absolute Shrinkage and Selection Operator (LASSO) estimator
of a predictive regression in which stock returns are conditioned on a large set of lagged …

We show that the adaptive Lasso is oracle efficient in stationary and nonstationary
autoregressions. This means that it estimates parameters consistently, selects the correct …

Although the Lasso has been extensively studied, the relationship between its prediction
performance and the correlations of the covariates is not fully understood. In this paper, we …

The lasso is a popular technique for simultaneous estimation and variable selection. Lasso
variable selection has been shown to be consistent under certain conditions. In this work we …

This paper proposes the nonnegative adaptive lasso method for variable selection both in
the classical fixed p setting (OLS initial estimator) and the ultra-high dimensional setting …

In theory, if penalty parameters are chosen appropriately then the lasso can eliminate
unnecessary variables in prediction problems, and improve the performance of predictors …

The adaptive Lasso is a commonly applied penalty for variable selection in regression
modeling. Like all penalties though, its performance depends critically on the choice of the …

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 …

The adaptive lasso is a model selection method shown to be both consistent in variable
selection and asymptotically normal in coefficient estimation. The actual variable selection …