The Annals of Statistics

Fitting time series models to nonstationary processes

R. Dahlhaus

Full-text: Open access

Abstract

A general minimum distance estimation procedure is presented for nonstationary time series models that have an evolutionary spectral representation. The asymptotic properties of the estimate are derived under the assumption of possible model misspecification. For autoregressive processes with time varying coefficients, the estimate is compared to the least squares estimate. Furthermore, the behavior of estimates is explained when a stationary model is fitted to a nonstationary process.

Article information

Source
Ann. Statist. Volume 25, Number 1 (1997), 1-37.

Dates
First available: 10 October 2002

Permanent link to this document
http://projecteuclid.org/euclid.aos/1034276620

Mathematical Reviews number (MathSciNet)
MR1429916

Digital Object Identifier
doi:10.1214/aos/1034276620

Zentralblatt MATH identifier
0871.62080

Subjects
Primary: 62M15: Spectral analysis
Secondary: 62F10: Point estimation

Keywords
Nonstationary processes time series evolutionary spectra minimum distance estimates model selection

Citation

Dahlhaus, R. Fitting time series models to nonstationary processes. The Annals of Statistics 25 (1997), no. 1, 1--37. doi:10.1214/aos/1034276620. http://projecteuclid.org/euclid.aos/1034276620.


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