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

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

The inputs to the TSMLOMAR subroutine are as follows:

*data*- specifies a
*T*×*M*data matrix, where*T*is the number of observations and*M*is the number of variables to be analyzed. *maxlag*- specifies the maximum lag of the vector AR (VAR) process.
This value should be less than [1/2
*M*] of 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 values of input series are zeroes. 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*VAR process is estimated. If*opt*[3]=1, a minimum AIC procedure is used. 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 estimates, minimum AIC, minimum AIC order, and innovation variance matrix.

The TSMLOMAR subroutine returns the following values:

*arcoef*- refers to an
*M*×(*M** nar) VAR coefficient vector of the final model if the intercept vector is not included. If*opt*[1]=1, the first column of the*arcoef*matrix is an intercept estimate vector. *ev*- refers to the error variance matrix.
*nar*- is the selected VAR 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
*data*, which corresponds to the first observation of the final model. *finish*- refers to the ending position of the input series
*data*, which corresponds to the last observation of the final model.

The TSMLOMAR subroutine analyzes nonstationary
(or locally stationary) multivariate time
series by using the minimum AIC procedure.
The data of length *T* is divided
into *J* locally stationary subseries.
See "Nonstationary Time Series" in
the "Nonstationary Time Series" section for details.

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