ECMvar: Error-Correction VAR Models In MTS: All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS) and Estimating Multivariate Volatility Models

Description

Performs estimation of an Error-Correction VAR(p) model using the Quasi Maximum Likelihood Method.

Usage

 ```1 2 3``` ```ECMvar(x, p, ibeta, include.const = FALSE, fixed = NULL, alpha = NULL, se.alpha = NULL, se.beta = NULL, phip = NULL, se.phip = NULL) ```

Arguments

 `x` A T-by-k data matrix of a k-dimensional co-integrated VAR process `p` VAR order `ibeta` Initial estimate of the co-integrating matrix. The number of columns of ibeta is the number of co-integrating series `include.const` A logical switch to include a constant term in the model. The default is no constant `fixed` A logical matrix to set zero parameter constraints. `alpha` Initial estimate of alpha, if any `se.alpha` Initial estimate of the standard error of alpha, if any `se.beta` Initial estimate of the standard error of beta, if any `phip` Initial estimate of the VAR coefficients, if any `se.phip` Initial estimate of the stanard error of the VAR coefficients, if any

Value

 `data` The vector time series `ncoint` The number of co-integrating series `arorder` VAR order `include.const` Logical switch to include constant `alpha,se.alpha` Estimates and their standard errors of the alpha matrix `beta,se.beta` Estimates and their standard errors of the beta matrix `aic,bic` Information criteria of the fitted model `residuals` The residual series `Sigma` Residual covariance matrix `Phip,se.Phip` Estimates and their standard errors of VAR coefficients

Ruey S. Tsay

References

Tsay (2014, Chapter 5)

 ```1 2 3 4 5 6 7``` ```phi=matrix(c(0.5,-0.25,-1.0,0.5),2,2); theta=matrix(c(0.2,-0.1,-0.4,0.2),2,2) Sig=diag(2) mm=VARMAsim(300,arlags=c(1),malags=c(1),phi=phi,theta=theta,sigma=Sig) zt=mm\$series[,c(2,1)] beta=matrix(c(1,0.5),2,1) m1=ECMvar(zt,3,ibeta=beta) names(m1) ```