# lsar.chgpt

From TSSS v1.2.3
by Masami Saga

##### Estimation of the Change Point

Precisely estimate a change point of subinterval for locally stationary AR model.

- Keywords
- ts

##### Usage

`lsar.chgpt(y, max.arorder = 20, subinterval, candidate, plot = TRUE, …)`

##### Arguments

- y
a univariate time series.

- max.arorder
highest order of AR model.

- subinterval
a vector of the form

`c(n0, ne)`

which gives a start and end point of time interval used for model fitting.- candidate
a vector of the form

`c(n1, n2)`

which gives minimum and maximum for change point.`n0+2k`

<`n1`

<`n2+k`

<`ne`

,(

`k`

is`max.arorder`

)- plot
logical. If

`TRUE`

(default),`y[n0:ne]`

and '`aic`

' are plotted.- …
further arguments to be passed to

`plot.chgpt`

.

##### Value

An object of class `"chgpt"`

, which is a list with the following
elements:

AICs of the AR model fitted on `[n1, n2]`

.

minimum AIC.

a change point.

original sub-interval data and information.

##### References

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

##### Examples

```
# NOT RUN {
# seismic data
data(MYE1F)
lsar.chgpt(MYE1F, max.arorder = 10, subinterval = c(200, 1000),
candidate = c(400, 800))
lsar.chgpt(MYE1F, max.arorder = 10, subinterval = c(600, 1400),
candidate = c(800, 1200))
# }
```

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

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