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Autoregressive Process Modeling via the Lasso Procedure
| Content Provider | CiteSeerX |
|---|---|
| Author | Nardi, Yuval Rinaldo, Alessandro |
| 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. 1 |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Autoregressive Coefficient Autoregressive Time Series Model Model Selection Consistent Popular Model Selection Derive Theoretical Result Lasso Estimator Nice Theoretical Property Estimation Procedure Prediction Consistent Linear Model Lasso Procedure Simulation Study Result Autoregressive Process Modeling Maximal Lag Estimation Consistent Double Asymptotic Framework |
| Content Type | Text |
