<p>I don't know a lot about VAR, but model selection is well-developed in the context of OLS and should be analogously applicable to VAR.</p>
<p><a href="https://en.wikipedia.org/wiki/Lasso_(statistics)" rel="nofollow noreferrer">LASSO</a> is the most commonly used method of model selection. The premise is to penalize choice of a non-zero coefficient for a variable. The variable only receives a nonzero coefficient if doing so reduces the sum of squared residuals in a meaningful manner.</p>
<p>The LASSO method also biases coefficients toward 0. To overcome this, there is a common two-step method of (1) estimating with LASSO to observe which variables have nonzero coefficients and (2) estimating the regular (non-LASSO) model while only restricting to coefficients chosen in the first step.</p>
<p><a href="https://projecteuclid.org/journals/bernoulli/volume-19/issue-2/Least-squares-after-model-selection-in-high-dimensional-sparse-models/10.3150/11-BEJ410.full" rel="nofollow noreferrer">Belloni and Chernozhukov</a> (2013) is a citation for this two-step approach.</p>

CC BY-SA 4.0
2022-05-09T08:25:38.167Z
2022-05-09T08:25:38.167Z
Michael Gmeiner
<p>Let s say I have a big VAR model with many variables. Then I run the model. How can I know which variables I should keep or get rid of if I want to ameliorate my model ?</p>
<p>What if my model has so many variables I can't remove it one variable after the other ? How to select the variables ?</p>
<p>Thank you</p>

CC BY-SA 4.0
2022-05-09T10:16:08.170Z
What is the purpose of your model? Why do you want to decrease the number of variables?
Dayne
CC BY-SA 4.0
2022-05-09T13:55:40.387Z
Here is a fairly modern approach to VAR estimation and model selection: Nicholson et al. ["High Dimensional Forecasting via Interpretable Vector
Autoregression"](https://www.jmlr.org/papers/volume21/19-777/19-777.pdf) (2020). Implementation wise there is e.g. the [`bigtime`](https://cran.r-project.org/web/packages/bigtime/index.html) package in R.
Richard Hardy
<macroeconomics><econometrics><time-series><forecasting><svar>
Which variable keep/get rid in a time series model?
BAL
# Which variable keep/get rid in a time series model?

(Asked by: username_0 on 2022-05-09)

Let s say I have a big VAR model with many variables. Then I run the model. How can I know which variables I should keep or get rid of if I want to ameliorate my model ?

What if my model has so many variables I can't remove it one variable after the other ? How to select the variables ?

Thank you

**Tags:** macroeconomics, econometrics, time-series, forecasting, svar

> **Comments:**
> **username_1**: What is the purpose of your model? Why do you want to decrease the number of variables?  
> **username_2**: Here is a fairly modern approach to VAR estimation and model selection: Nicholson et al. ["High Dimensional Forecasting via Interpretable Vector Autoregression"](https://www.jmlr.org/papers/volume21/19-777/19-777.pdf) (2020). Implementation wise there is e.g. the [`bigtime`](https://cran.r-project.org/web/packages/bigtime/index.html) package in R.  

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## 1 answers

### Answer by username_3 on 2022-05-09. Score: 1

I don't know a lot about VAR, but model selection is well-developed in the context of OLS and should be analogously applicable to VAR.

[LASSO](https://en.wikipedia.org/wiki/Lasso_(statistics)) is the most commonly used method of model selection. The premise is to penalize choice of a non-zero coefficient for a variable. The variable only receives a nonzero coefficient if doing so reduces the sum of squared residuals in a meaningful manner.

The LASSO method also biases coefficients toward 0. To overcome this, there is a common two-step method of (1) estimating with LASSO to observe which variables have nonzero coefficients and (2) estimating the regular (non-LASSO) model while only restricting to coefficients chosen in the first step.

[Belloni and Chernozhukov](https://projecteuclid.org/journals/bernoulli/volume-19/issue-2/Least-squares-after-model-selection-in-high-dimensional-sparse-models/10.3150/11-BEJ410.full) (2013) is a citation for this two-step approach.


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