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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.

Example:

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

References

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.

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Install

install.packages('VARshrink')

Monthly Downloads

154

Version

0.3.1

License

GPL-3

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Maintainer

Namgil Lee

Last Published

October 9th, 2019

Functions in VARshrink (0.3.1)

lm_multiv_ridge

Multivariate Ridge Regression
lm_full_Bayes_SR

Full Bayesian Shrinkage Estimation Method for Multivariate Regression
lm_ShVAR_KCV

K-fold Cross Validation for Selection of Shrinkage Parameters of Semiparametric Bayesian Shrinkage Estimator for Multivariate Regression
irf.varshrinkest

Impulse response function
restrict_sh

Restricted VAR
arch.test_sh

ARCH-LM test
print.varshsum

Print method for class "varshsum"
fevd.varshrinkest

Forecast Error Variance Decomposition
normality.test_sh

Normality, multivariate skewness and kurtosis test
logLik.varshrinkest

Log-likelihood method for class "varshrinkest"
roots_sh

Eigenvalues of the companion coefficient matrix of a VAR(p)-process
serial.test_sh

Test for serially correlated errors for VAR shrinkage estimate
predict.varshrinkest

Predict method for objects of class varshrinkest
lm_semi_Bayes_PCV

Semiparametric Bayesian Shrinkage Estimation Method for Multivariate Regression
stability_sh

Stability function
summary.shrinklm

Summary method for class "shrinklm"
summary.varshrinkest

Summary method for an object of class 'varshrinkest', VAR parameters estimated by VARshrink()
simVARmodel

Generate multivariate time series data using the given VAR model
shrinkVARcoef

Semiparametric Bayesian Shrinkage Estimator for Multivariate Regression
print.varshrinkest

Print method for class "varshrinkest"
Acoef_sh

Coefficient matrices of endogenous variables
causality_sh

Causality Analysis for class "varshrinkest"
calcSSE_Acoef

Sum of squared errors (SSE) between coefficients of two VARs
BQ_sh

BQ function for class "varshrinkest"
convPsi2varresult

Convert format for VAR coefficients from Psi to varresult
Phi.varshrinkest

Coefficient matrices of the MA represention
Bcoef_sh

Coefficient matrix
createVARCoefs_ltriangular

Create coefficients of a VAR model
VARshrink

Shrinkage estimation of VAR parameters