Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2021
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2105.00860 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917698955378688 |
|---|---|
| author | Ballarin, Giovanni |
| author_facet | Ballarin, Giovanni |
| contents | Ridge regression is a popular method for dense least squares regularization. In this work, ridge regression is studied in the context of VAR model estimation and inference. The implications of anisotropic penalization are discussed and a comparison is made with Bayesian ridge-type estimators. The asymptotic distribution and the properties of cross-validation techniques are analyzed. Finally, the estimation of impulse response functions is evaluated with Monte Carlo simulations and ridge regression is compared with a number of similar and competing methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2105_00860 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Ridge Regularized Estimation of VAR Models for Inference Ballarin, Giovanni Methodology Ridge regression is a popular method for dense least squares regularization. In this work, ridge regression is studied in the context of VAR model estimation and inference. The implications of anisotropic penalization are discussed and a comparison is made with Bayesian ridge-type estimators. The asymptotic distribution and the properties of cross-validation techniques are analyzed. Finally, the estimation of impulse response functions is evaluated with Monte Carlo simulations and ridge regression is compared with a number of similar and competing methods. |
| title | Ridge Regularized Estimation of VAR Models for Inference |
| topic | Methodology |
| url | https://arxiv.org/abs/2105.00860 |