# marfit

From TSSS v1.2.3
by Masami Saga

##### Yule-Walker Method of Fitting Multivariate AR Model Fitting

Fit a multivariate AR model by Yule-Walker method.

- Keywords
- ts

##### 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:

order of minimum AIC.

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

.

innovation covariance matrix of AIC best model.

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)
# }
```

*Documentation reproduced from package TSSS, version 1.2.3, License: GPL (>= 2)*

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