# marlsq

From TSSS v1.2.3
by Masami Saga

##### Least Squares Method for Multivariate AR Model

Fit a multivariate AR model by least squares method.

- Keywords
- ts

##### 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:

order of the MAICE model.

total AIC of the model.

innovation covariance matrix.

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)
# }
```

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

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