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Fitting time series models to nonstationary processes
The Annals of Statistics · 1997 · Vol. 25(1)
Rainer Dahlhaus✉(Heidelberg University)
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.
Complex Systems and Time Series AnalysisControl Systems and IdentificationNeural Networks and ApplicationsMathematicsSeries (stratigraphy)Autoregressive modelApplied mathematicsSTAR modelRepresentation (politics)Autoregressive–moving-average modelAutoregressive integrated moving averageLeast-squares function approximationTime series
Funding
- Deutsche Forschungsgemeinschaft
Citations
922
FWCI
39.49
field-weighted impact
References
33
Percentile
100%
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Citations per year
References
Evolutionary Spectra and Non-Stationary Processes
Journal of the Royal Statistical Society Series B (Statistical Methodology) · 1965 · 1,038 citations
Time Series: Theory and Methods
Technometrics · 1992 · 5,091 citations
A new look at the statistical model identification
IEEE Transactions on Automatic Control · 1974 · 49,965 citations
Spectral Analysis and Time Series
Technometrics · 1983 · 4,850 citations
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