Semiparametric Efficient Estimation of the Mean of a Time Series in the Presence of Conditional Heterogeneity of Unknown Form

Douglas Hodgson ()

No 146, Cahiers de recherche CREFE / CREFE Working Papers from CREFE, Université du Québec à Montréal

Abstract: We obtain semiparametric efficiency bounds for the estimation of a location parameter in a time series model where the innovations are stationary and ergodic, conditionally symmetric martingale differences but otherwise possess general dependence and distributions of unknown form. We then describe an iterative estimator that achieves this bound when the conditional density functions of the sample are known. Finally, we develop a 'semi-adaptive' estimator that achieves the bound when these densities are unknown by the investigator. This estimator employs nonparametric kernel estimates of the densities. Monte Carlo results are reported.

Nous dérivons une borne d'efficacité semi-paramétrique pour un estimateur de la moyenne d'une série temporelle dans un contexte où les innovations sont stationnaires et ergodiques. Nous supposons que les innovations suivent de façon conditionelle une différence de martingale symétrique tout en permettant une forme générale de dépendence temporelle et un loi de forme inconnue. Un estimateur «semi-adaptif» atteignant cette borne est proposé dans le cas où le fonction de densité est inconnue. Cet estimateur utilise une estimation non-paramétrique à noyau de la fonction de densité. Enfin, une étude de Monte-Carlo est présentée.

Keywords: Time series; semiparametric efficiency; conditional heteroskedasticity; adaptive estimation. (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2002-01
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-pke
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