Semiparametric Estimation of the Intensity of Long Memory in Conditional Heteroskedasticity
AbstractThe paper is concerned with the estimation of the long memory parameter in a conditionally heteroskedastic model proposed by Giraitis, Robinson and Surgailis (1999). We consider methods based on the partial sums of the squared observations which are similar in spirit to the classicla R/S analysis as well as spectral domain approximate maximum likelihood estimators. The finite sample performance of the estimators is examined by means of a Monte Carlo study.
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Bibliographic InfoArticle provided by Springer in its journal Statistical Inference for Stochastic Processes.
Volume (Year): 3 (2000)
Issue (Month): 1 (January)
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Web page: http://www.springerlink.com/link.asp?id=102997
Other versions of this item:
- Giraitis, L. & Kokoszka, P. & Leipus, R. & Teyssiere, G., 1999. "Semiparametric Estimation of the Intensity of Long Memory in Conditional Heteroskedasticity," G.R.E.Q.A.M. 99a24, Universite Aix-Marseille III.
- Giraitis, Liudas & Kokoszka, Piotr & Leipus, Remigijus & Teyssière, Gilles, 1999. "Semiparametric estimation of the intensity of long memory in conditional heteroskedasticity," SFB 373 Discussion Papers 1999,81, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
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