marlsq: Least Squares Method for Multivariate AR Model

In TSSS: Time Series Analysis with State Space Model

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 components:

maice.order	order of the MAICE model.
aic	AIC of the MAR model with minimum AIC orders.
v	innovation covariance matrix.
arcoef	AR coefficient matrices.

References

Kitagawa, G. (2020) Introduction to Time Series Modeling with Applications in R. Chapman & Hall/CRC.

Examples

# 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)

TSSS documentation built on Sept. 29, 2023, 9:07 a.m.