VARshrink: Shrinkage Estimation Methods for Vector Autoregressive (VAR) Models.

VAR model is a fundamental and effective approach for multivariate time series analysis. Shrinkage estimation methods can be applied to high-dimensional VAR models with dimensionality greater than the number of observations, contrary to the standard ordinary least squares method.

The package VARshrink aims to be an integrative R package delivering nonparametric, parametric, and semiparametric methods in a unified and consistent manner.

The package VARshrink provides a simple interface function VARshrink(), which is an extension of the function VAR() in the vars package.


data(Canada, package = "vars")
y <- diff(Canada)
estim <- VARshrink(y, p = 2, type = "const", method = "ns")
plot(predict(estim), names = "U")


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R. Opgen-Rhein and K. Strimmer (2007). Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregressive process. BMC Bioinformatics 8(2), S3. doi: 10.1186/1471-2105-8-S2-S3.