analyzes nonstationary or locally stationary time series
by using the minimum AIC procedure
- CALL TSMLOCAR( arcoef, ev, nar, aic, start, finish, data
- <,maxlag, opt, missing,
The inputs to the TSMLOCAR subroutine are as follows:
- specifies a T ×1 (or 1 ×T) data vector.
- 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.
- specifies an options vector.
- specifies the mean deletion option.
The mean of the original data is deleted if opt=-1.
An intercept coefficient is estimated if opt=1.
If opt=0, the original input data is processed
assuming that the mean value of the input series is 0.
The default is opt=0.
- specifies the number (J) of basic spans.
By default, opt=1.
- specifies the minimum AIC option.
If opt=0, the maximum lag
AR process is estimated.
If opt=1, the minimum AIC procedure is performed.
The default is opt=1.
- 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:
- refers to an nar ×1 AR coefficient vector
of the final model if the intercept estimate is not included.
If opt=1, the first element of the
arcoef vector is an intercept estimate.
- refers to the error variance.
- is the selected AR order of the final model.
If opt=0, nar=maxlag.
- refers to the minimum AIC value of the final model.
- refers to the starting position of the input series, which
corresponds to the first observation of the final model.
- 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.
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.