Vector autoregressive (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. This package is an integrative package delivering nonparametric,
parametric, and semiparametric methods in a unified and consistent manner,
such as the multivariate ridge regression in Golub, Heath, and Wahba (1979)
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.
The package can be installed conveniently from GitHub:
if (!("devtools" %in% installed.packages()))
install.packages("devtools")
devtools::install_github("namgillee/VARshrink")
data(Canada, package = "vars")
y <- diff(Canada)
estim <- VARshrink(y, p = 2, type = "none", method = "ns")
plot(predict(estim), names = "U")
The package can be tested by
devtools::test()
Style violations and syntax errors can be checked by
lintr::lint_package()
N. Lee, H. Choi, and S.-H. Kim (2016). Bayes shrinkage estimation for high-dimensional VAR models with scale mixture of normal distributions for noise. Computational Statistics & Data Analysis 101, 250-276. doi: 10.1016/j.csda.2016.03.007
S. Ni and D. Sun (2005). Bayesian estimates for vector autoregressive models. Journal of Business & Economic Statistics 23(1), 105-117. doi: 10.1198/073500104000000622
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.