Shrinkage Estimation Methods for Vector Autoregressive Models
Coefficient matrices of endogenous variables
ARCH-LM test
Coefficient matrix
BQ function for class "varshrinkest"
Sum of squared errors (SSE) between coefficients of two VARs
Causality Analysis for class "varshrinkest"
Convert format for VAR coefficients from Psi to varresult
Create coefficients of a VAR model
Forecast Error Variance Decomposition
Impulse response function
Full Bayesian Shrinkage Estimation Method for Multivariate Regression
Multivariate Ridge Regression
Semiparametric Bayesian Shrinkage Estimation Method for Multivariate R...
K-fold Cross Validation for Selection of Shrinkage Parameters of Semip...
Log-likelihood method for class "varshrinkest"
Normality, multivariate skewness and kurtosis test
Coefficient matrices of the MA represention
Predict method for objects of class varshrinkest
Print method for class "varshrinkest"
Print method for class "varshsum"
Restricted VAR
Eigenvalues of the companion coefficient matrix of a VAR(p)-process
Test for serially correlated errors for VAR shrinkage estimate
Semiparametric Bayesian Shrinkage Estimator for Multivariate Regressio...
Generate multivariate time series data using the given VAR model
Stability function
Summary method for class "shrinklm"
Summary method for an object of class 'varshrinkest', VAR parameters e...
Shrinkage estimation of VAR parameters
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) <doi:10.2307/1268518>, a James-Stein type nonparametric shrinkage method in Opgen-Rhein and Strimmer (2007) <doi:10.1186/1471-2105-8-S2-S3>, and Bayesian estimation methods using noninformative and informative priors in Lee, Choi, and S.-H. Kim (2016) <doi:10.1016/j.csda.2016.03.007> and Ni and Sun (2005) <doi:10.1198/073500104000000622>.