Article ID Journal Published Year Pages File Type
1146048 Journal of Multivariate Analysis 2011 22 Pages PDF
Abstract

The Lasso is a popular model selection and estimation procedure for linear models that enjoys nice theoretical properties. In this paper, we study the Lasso estimator for fitting autoregressive time series models. We adopt a double asymptotic framework where the maximal lag may increase with the sample size. We derive theoretical results establishing various types of consistency. In particular, we derive conditions under which the Lasso estimator for the autoregressive coefficients is model selection consistent, estimation consistent and prediction consistent. Simulation study results are reported.

Related Topics
Physical Sciences and Engineering Mathematics Numerical Analysis
Authors
, ,