Language Reference

TSMLOMAR Call

analyzes nonstationary or locally stationary multivariate time series by using the minimum AIC procedure

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 x 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 \frac{1}{2m} 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 are processed assuming that the mean values of input series are zeroes. The default is opt[1]=0.

opt[2]
specifies the span length to be used when breaking up the time series into separate blocks. By default, opt[2]=0, which forces all of the time series values into a single span.

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 x (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 section "Nonstationary Time Series" for details.

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