Dahlhaus, Rainer ; Wefelmeyer, Wolfgang
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Abstract
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.
| Document 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 > Institut für Mathematik |
| DDC-classification: | 310 General statistics 510 Mathematics |
| Uncontrolled Keywords: | Time series, misspecified models, efficiency, minimum distance estimation, maximum likelihood, prediction |
| Series: | Beiträge zur Statistik > Beiträge |
| Additional Information: | Erschien auch in: The Annals of Statistics 1996, Vol. 24, No. 3, S. 952-974 |
