TSSS (version 1.2.3)

marlsq: Least Squares Method for Multivariate AR Model

Description

Fit a multivariate AR model by least squares method.

Usage

marlsq(y, lag = NULL)

Arguments

y

a multivariate time series.

lag

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

Value

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

maice.order

order of the MAICE model.

aic

total AIC of the model.

v

innovation covariance matrix.

arcoef

AR coefficient matrices.

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)
y <- as.matrix(HAKUSAN[, c(1,2,4)])   # Yaw rate, Rolling, Rudder angle
z <- marlsq(y)
z

marspc(z$arcoef, v = z$v)
# }