REGts function

Regression Model with Time Series Errors

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

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

Returns

• 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

References

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

Author(s)

Ruey S. Tsay