Estimation of k-factor GIGARCH process : a Monte Carlo study
AbstractIn this paper, we discuss the parameter estimation for a k-factor generalized long memory process with conditionally heteroskedastic noise. Two estimation methods are proposed. The first method is based on the conditional distribution of the process and the second is obtained as an extension of Whittle's estimation approach. For comparison purposes, Monte Carlo simulations are used to evaluate the finite sample performance of these estimation techniques.
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Bibliographic InfoPaper provided by Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne in its series Documents de travail du Centre d'Economie de la Sorbonne with number b08004.
Length: 18 pages
Date of creation: Jan 2008
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Long memory; Gegenbauer polynomial; heteroskedasticity; conditional sum of squares; Whittle estimation.;
Other versions of this item:
- Abdou Kâ Diongue & Dominique Guegan, 2008. "Estimation of k-factor GIGARCH process : a Monte Carlo study," UniversitÃ© Paris1 PanthÃ©on-Sorbonne (Post-Print and Working Papers) halshs-00235179, HAL.
- Diongue Abdou Ka & Dominique Guegan, 2008. "Estimation of k-Factor Gigarch Process: A Monte Carlo Study," UniversitÃ© Paris1 PanthÃ©on-Sorbonne (Post-Print and Working Papers) halshs-00375758, HAL.
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-02-16 (All new papers)
- NEP-ECM-2008-02-16 (Econometrics)
- NEP-ETS-2008-02-16 (Econometric Time Series)
- NEP-ORE-2008-02-16 (Operations Research)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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