SCMfit function

Scalar Component Model Fitting

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)

Arguments

• da: The T-by-k data matrix of a k-dimensional time series
• scms: A k-by-2 matrix of the orders of SCMs
• Tdx: A k-dimensional vector for locating "1" of each row in the transformation matrix.
• include.mean: A logical switch to include the mean vector. Default is to include mean vector.
• fixed: A logical matrix to set parameters to zero
• prelim: A logical switch for preliminary estimation. Default is false.
• details: A logical switch to control details of output
• thres: Threshold for individual t-ratio when setting parameters to zero. Default is 1.
• ref: A switch to use SCMmod in model specification.
• SCMpar: Parameter estimates of the SCM model, to be used in model refinement
• seSCMpar: Standard errors of the parameter estimates in SCMpar

Details

Perform conditional maximum likelihood estimation of a VARMA model specified by the scalar component model approach, including the transformation matrix.

Returns

• data: Observed time series

• SCMs: The specified SCMs

• Tdx: Indicator vector for the transformation matrix. The length of Tdx is k.

• locTmtx: Specification of estimable parameters of the transformation matrix

• locAR: Locators for the estimable parameters of the VAR coefficients

• locMA: Locators for the estimable parameters of the VMA coefficients

• cnst: A logical switch to include the constant vector in the model

• coef: The parameter estimates

• secoef: Standard errors of the parameter estimates

• residuals: Residual series

• Sigma: Residual covariance matrix

• aic,bic: Information criteria of the fitted model

• Ph0: Estimates of the constant vector, if any

• Phi: Estimates of the VAR coefficients

• Theta: Estimates of the VMA coefficients

References

Tsay (2014, Chapter 4). Multivariate Time Series Analysis with R and Financial Applications. John Wiley. Hoboken, NJ.

Author(s)

Ruey S. Tsay