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Asymptotically Optimal Estimation in Misspecified Time Series Models

Dahlhaus, Rainer ; Wefelmeyer, Wolfgang

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A concept of asymptotically efficient estimation is presented whena misspecified parametric time series model is fitted to a stationary process.Efficiency of several minimum distance estimates is proved and the behavior ofthe Gaussian maximum likelihood estimate is studied. Furthermore, the behaviorof estimates that minimize the h-step prediction error is discussed briefly. The paper answers to some extent the question what happens when a misspecifiedmodel is fitted to time series data and one acts as if the model were true.

Item Type: Working paper
Place of Publication: Heidelberg
Date Deposited: 16 Jun 2016 07:13
Date: 1996
Number of Pages: 25
Faculties / Institutes: The Faculty of Mathematics and Computer Science > Department of Applied Mathematics
Subjects: 310 General statistics
510 Mathematics
Uncontrolled Keywords: Time series, misspecified models, efficiency, minimum distance estimation, maximum likelihood, prediction
Schriftenreihe ID: Beiträge zur Statistik > Beiträge
Additional Information: Erschien auch in: The Annals of Statistics 1996, Vol. 24, No. 3, S. 952-974
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