TSSS (version 1.2.3)

marfit: Yule-Walker Method of Fitting Multivariate AR Model Fitting

Description

Fit a multivariate AR model by Yule-Walker method.

Usage

marfit(y, lag = NULL)

Arguments

y

a multivariate time series.

lag

highest order of fitted AR models. Default is \(2 \sqrt{n}\), where \(n\) is the length of the time series y.

Value

An object of class "maryule", which is a list with the following elements:

maice.order

order of minimum AIC.

aic

AIC's of the AR model with order \(0,\dots,\)lag.

v

innovation covariance matrix of AIC best model.

arcoef

AR coefficient of the AIC best model.

References

Kitagawa, G. (2010) Introduction to Time Series Modeling. Chapman & Hall/CRC.

Examples

# NOT RUN {
# Yaw rate, rolling, pitching and rudder angle of a ship
data(HAKUSAN)
yy <- as.matrix(HAKUSAN[, c(1,2,4)])   # Yaw rate, Pitching, Rudder angle
nc <- dim(yy)[1]
n <- seq(1, nc, by = 2) 
y <- yy[n, ]
marfit(y, 20)
# }