analyzes time series that are nonstationary
in the covariance function
- CALL TSTVCAR( arcoef, variance, est, aic, data
- <,nar, init, opt, outlier,
The inputs to the TSTVCAR subroutine are as follows:
- specifies a T ×1 (or 1 ×T) data vector.
- specifies the order of the AR process.
The default is nar=8.
- specifies the initial values of the parameter estimates.
The default is (1E-4, 0.3, 1E-5, 0).
- specifies an options vector.
- specifies the mean deletion option.
The mean of the original series is subtracted
from the series if opt=-1.
By default, the original series is processed (opt=0).
- specifies the filtering period (nfilter).
The number of state vectors is
determined by [T/(nfilter)].
The default is opt=10.
- specifies the numerical differentiation method.
If opt=1, the one-sided
(forward) differencing method is used.
The two-sided (or central) differencing
method is used if opt=2.
The default is opt=1.
- specifies the vector of outlier observations.
The value should be less than or equal
to the maximum number of observations.
The default is outlier=0.
- specifies the print option.
By default, printed output is suppressed (print=0).
The print=1 option prints the final estimates.
The iteration history is printed if print=2.
The TSTVCAR subroutine returns the following values:
- refers to the time-varying AR coefficients.
- refers to the time-varying error variances.
See the "Smoothness Priors Modeling" section for details.
- refers to the parameter estimates.
- refers to the value of AIC from the final estimates.
Nonstationary time series modeling usually
deals with nonstationarity in the mean.
The TSTVCAR subroutine analyzes the model
that is nonstationary in the covariance.
Smoothness priors are imposed on each time-varying
AR coefficient and frequency response function.
See the "Nonstationary Time Series" section for details.
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.