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**CALL TSMLOCAR(***arcoef, ev, nar, aic, start, finish, data*

*<,maxlag, opt, missing, print>***);**

The inputs to the TSMLOCAR subroutine are as follows:

*data*- specifies a
*T*×1 (or 1 ×*T*) data vector. *maxlag*- specifies the maximum lag of the AR process.
This value should be less than half
the length of locally stationary spans.
The default is
*maxlag*=10. *opt*- specifies an options vector.

*opt*[1]- specifies the mean deletion option.
The mean of the original data is deleted if
*opt*[1]=-1. An intercept coefficient is estimated if*opt*[1]=1. If*opt*[1]=0, the original input data is processed assuming that the mean value of the input series is 0. The default is*opt*[1]=0.

*opt*[2]- specifies the number (
*J*) of basic spans. By default,*opt*[2]=1.

*opt*[3]- specifies the minimum AIC option.
If
*opt*[3]=0, the*maximum lag*AR process is estimated. If*opt*[3]=1, the minimum AIC procedure is performed. The default is*opt*[3]=1.

*missing*- specifies the missing value option.
By default, only the first contiguous observations
with no missing values are used (
*missing*=0). The*missing*=1 option ignores observations with missing values. If you specify the*missing*=2 option, the missing values are replaced with the sample mean.*print*] specifies the print option. By default, printed output is suppressed (*print*=0). The*print*=1 option prints the AR estimation result, while the*print*=2 option plots the power spectral density as well as the AR estimates.

The TSMLOCAR subroutine returns the following values:

*arcoef*- refers to an nar ×1 AR coefficient vector
of the final model if the intercept estimate is not included.
If
*opt*[1]=1, the first element of the*arcoef*vector is an intercept estimate. *ev*- refers to the error variance.
*nar*- is the selected AR order of the final model.
If
*opt*[3]=0,*nar*=*maxlag*. *aic*- refers to the minimum AIC value of the final model.
*start*- refers to the starting position of the input series, which
corresponds to the first observation of the final model.
*finish*- refers to the ending position of the input series, which corresponds to the last observation of the final model.

The TSMLOCAR subroutine analyzes nonstationary (or locally
stationary) time series by using the minimum AIC procedure.
The data of length *T* is divided into *J* locally stationary
subseries, which consist of [*T*/*J*] observations.
See the "Nonstationary Time Series" section for details.

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