REGts: Regression Model with Time Series Errors

REGtsR 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.


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