REGts: Regression Model with Time Series Errors

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

Regression Model with Time Series Errors

Description

Perform the maximum likelihood estimation of a multivariate linear regression model with time-series errors

Usage

REGts(zt, p, xt, include.mean = T, fixed = NULL, par = NULL, se.par = NULL, details = F)

Arguments

zt	A T-by-k data matrix of a k-dimensional time series
p	The VAR order
xt	A T-by-v data matrix of independent variables, where v denotes the number of independent variables (excluding constant 1).
include.mean	A logical switch to include the constant term. Default is to include the constant term.
fixed	A logical matrix used to set parameters to zero
par	Initial parameter estimates of the beta coefficients, if any.
se.par	Standard errors of the parameters in par, if any.
details	A logical switch to control the output

Details

Perform the maximum likelihood estimation of a multivariate linear regression model with time series errors. Use multivariate linear regression to obtain initial estimates of regression coefficients if not provided

Value

data	The observed k-dimensional time series
xt	The data matrix of independent variables
aror	VAR order
include.mean	Logical switch for the constant vector
Phi	The VAR coefficients
se.Phi	The standard errors of Phi coefficients
beta	The regression coefficients
se.beta	The standard errors of beta
residuals	The residual series
Sigma	Residual covariance matrix
coef	Parameter estimates, to be used in model simplification.
se.coef	Standard errors of parameter estimates

Author(s)

Ruey S. Tsay

References

Tsay (2014, Chapter 6). Multivariate Time Series Analysis with R and Financial Applications. John Wiley. Hoboken NJ.

MTS documentation built on April 11, 2022, 5:07 p.m.