Iterative plug-in algorithms for SEMIFAR models - definition, convergence and asymptotic properties

In this paper data-driven algorithms for fitting SEMIFAR models (Beran, 1999) are proposed. The algorithms combine the data-driven estimation of the nonparamet- ric trend and maximum likelihood estimation of the parameters. Convergence and asymptotic properties of the proposed algorithms are investigated. A large simulation study illustrates the practical performance of the methods.

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Year of publication:	2001
Authors:	Beran, Jan ; Feng, Yuanhua
Publisher:	Konstanz : University of Konstanz, Center of Finance and Econometrics (CoFE)
Subject:	Nichtparametrisches Verfahren \| Modellierung \| Theorie \| ARMA-Modell \| semiparametric models \| long-range dependence \| fractional ARIMA \| antipersistence \| nonparametric regression \| bandwidth selection

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Series:	CoFE Discussion Paper ; 01/11
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Working Paper
Language:	English
Other identifiers:	870126792 [GVK] hdl:10419/85212 [Handle] RePEc:zbw:cofedp:0111 [RePEc]
Source:	EconStor

Persistent link: https://www.econbiz.de/10010324077