Perform estimation of a VARMA model specified via the SCM approach
SCMfit(da, scms, Tdx, include.mean = T, fixed = NULL,
prelim = F, details = F, thres = 1, ref = 0,
SCMpar=NULL, seSCMpar=NULL)The T-by-k data matrix of a k-dimensional time series
A k-by-2 matrix of the orders of SCMs
A k-dimensional vector for locating "1" of each row in the transformation matrix.
A logical switch to include the mean vector. Default is to include mean vector.
A logical matrix to set parameters to zero
A logical switch for preliminary estimation. Default is false.
A logical switch to control details of output
Threshold for individual t-ratio when setting parameters to zero. Default is 1.
A switch to use SCMmod in model specification.
Parameter estimates of the SCM model, to be used in model refinement
Standard errors of the parameter estimates in SCMpar
Observed time series
The specified SCMs
Indicator vector for the transformation matrix. The length of Tdx is k.
Specification of estimable parameters of the transformation matrix
Locators for the estimable parameters of the VAR coefficients
Locators for the estimable parameters of the VMA coefficients
A logical switch to include the constant vector in the model
The parameter estimates
Standard errors of the parameter estimates
Residual series
Residual covariance matrix
Information criteria of the fitted model
Estimates of the constant vector, if any
Estimates of the VAR coefficients
Estimates of the VMA coefficients
Perform conditional maximum likelihood estimation of a VARMA model specified by the scalar component model approach, including the transformation matrix.
Tsay (2014, Chapter 4). Multivariate Time Series Analysis with R and Financial Applications. John Wiley. Hoboken, NJ.