| REGts {MTS} | R Documentation |
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.