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 processwith 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, using four different conditional distribution functions.
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Bibliographic InfoPaper provided by HAL in its series Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) with number halshs-00375758.
Date of creation: 2008
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Publication status: Published, Communications in Statistics - Simulations and Computations, 2008, 37, 10, 2037-2049
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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.
- Abdou Kâ Diongue & Dominique Guegan, 2008. "Estimation of k-factor GIGARCH process : a Monte Carlo study," Documents de travail du Centre d'Economie de la Sorbonne b08004, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
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