A concept of asymptotically efficient estimation is presented when a misspecified parametric time series model is fitted to a stationary process. Efficiency of several minimum distance estimates is proved and the behavior of the Gaussian maximum likelihood estimate is studied. Furthermore, the behavior of estimates that minimize the h-step prediction error is discussed briefly. The paper answers to some extent the question what happens when a misspecified model is fitted to time series data and one acts as if the model were true.
Publié le : 1996-06-14
Classification:
Time series,
misspecified models,
efficiency,
minimum distance estimation,
maximum likelihood,
prediction,
62M10,
62G20
@article{1032526951,
author = {Dahlhaus, R. and Wefelmeyer, W.},
title = {Asymptotically optimal estimation in misspecified time series models},
journal = {Ann. Statist.},
volume = {24},
number = {6},
year = {1996},
pages = { 952-974},
language = {en},
url = {http://dml.mathdoc.fr/item/1032526951}
}
Dahlhaus, R.; Wefelmeyer, W. Asymptotically optimal estimation in misspecified time series models. Ann. Statist., Tome 24 (1996) no. 6, pp. 952-974. http://gdmltest.u-ga.fr/item/1032526951/