Scalar Component Model Fitting

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Description

Perform estimation of a VARMA model specified via the SCM approach

Usage

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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.

Value

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

Author(s)

Ruey S. Tsay

References

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

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