# Ridge Regularized Estimation of VAR Models for Inference

arXiv: https://arxiv.org/abs/2105.00860 · HTML: https://mlsanity.com/paper/2105.00860/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2021.

## Built on by (most cited first, 1 of 1)

- [From Causal Discovery to Dynamic Causal Inference in Neural Time Series](https://mlsanity.com/paper/2603.20980/) (2026)

## Abstract (arXiv)

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.