VARs in R

I am somewhat irked by the lack of a comprehensive R package for multivariate time series. Rob Hyndman’s forecast/ fable package is an excellent, if not exhaustive (how could it really be?), resource for univariate time series.

Here, I am collecting a list of packages that work multivariate time series models, particularly vector autoregressions (VARs).

vars: Standard frequentist VARs and Sturctural VARs (SVARs). Normal VARs are estimated equation-by-equation by least squares, so a “varest” object is basically a collection of “lm” objects that pull from a common data matrix. The “VARselect” function is very useful for lag order selection via information criteria. Includes useful statistical tests for residual serial correlation, normality, and autoregressive conditional heteroskedasticity.

urca: Unit Root and Cointegration Analysis. Useful implementations of the Johansen cointegreation test and estimation of vector error correction models (VECMs). The “vec2var” function converts a VECM to its equivalent VAR representation.

BVAR: Straight-forward estimation and forecasting of Bayesian VARs with customizable, Minnesota-type priors. Hierarchical estimation in the fashion of Giannone, Lenza & Primiceri (2015).

bvarsv: Computes Bayesian VARs with stochastic volatility and time-varying parameters.

tvReg: The function “tvVAR” implements a time-varying-parameters VAR using kernel smoothing.

HDEconometrics: Allows for easy estimation via LASSO through “ic.glmnet” and “HDVar”.

More to come.

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