sVARMACpp: Seasonal VARMA Model Estimation (Cpp)

In MTS: All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS) and Estimating Multivariate Volatility Models

Seasonal VARMA Model Estimation (Cpp)

Description

Performs conditional maximum likelihood estimation of a seasonal VARMA model. This is the same function as sVARMA, with the likelihood function implemented in C++ for efficiency.

Usage

sVARMACpp(da, order, sorder, s, include.mean = T, fixed = NULL, details = F, switch = F)

Arguments

da	A T-by-k data matrix of a k-dimensional seasonal time series
order	Regular order (p,d,q) of the model
sorder	Seasonal order (P,D,Q) of the model
s	Seasonality. s=4 for quarterly data and s=12 for monthly series
include.mean	A logical switch to include the mean vector. Default is to include the mean
fixed	A logical matrix to set zero parameter constraints
details	A logical switch for output
switch	A logical switch to exchange the ordering of the regular and seasonal VMA factors. Default is theta(B)*Theta(B).

Details

Estimation of a seasonal VARMA model

Value

data	The data matrix of the observed k-dimensional time series
order	The regular order (p,d,q)
sorder	The seasonal order (P,D,Q)
period	Seasonality
cnst	A logical switch for the constant term
ceof	Parameter estimates for use in model simplification
secoef	Standard errors of the parameter estimates
residuals	Residual series
Sigma	Residual covariance matrix
aic,bic	Information criteria of the fitted model
regPhi	Regular AR coefficients, if any
seaPhi	Seasonal AR coefficients
regTheta	Regular MA coefficients
seaTheta	Seasonal MA coefficients
Ph0	The constant vector, if any
switch	The logical switch to change the ordering of matrix product

Author(s)

Ruey S. Tsay

References

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

See Also

sVARMA

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