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

1
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. Deafult 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 sesonal VARMA model

Value

data

The data matrix of the observed k-dimensional time series

order

The reqular 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

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