Conditional variance estimation in regression models with long memory

• Verfasst von: Kulik, Rafal [VerfasserIn]
• Verfasst von: Wichelhaus, Cornelia [VerfasserIn]
• Titel: Conditional variance estimation in regression models with long memory
• Verf.angabe: Rafal Kulik and Cornelia Wichelhaus
• Umfang: 16 S.
• Fussnoten: Gesehen am 30.05.2018
• Titel Quelle: Enthalten in: Journal of time series analysis
• Jahr Quelle: 2012
• Band/Heft Quelle: 33, 3, S. 468-483
• ISSN Quelle: 1467-9892
• DOI: doi:10.1111/j.1467-9892.2012.00782.x
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1575864614

Abstract

In this article we study asymptotic properties of a non-parametric kernel estimator of the conditional variance in a random design model with parametric mean and heteroscedastic errors, for a class of long-memory errors and predictors. We establish small and large bandwidths asymptotics, which show a different behaviour compared with that of kernel estimators of the conditional mean. We distinguish between an oracle case (i.e. where the errors are directly observed) and a non-oracle case (where the errors are replaced with residuals) and show non-equivalence between the oracle and non-oracle case. We also discuss a practical problem of bandwidth choice. Theoretical results are justified by simulation studies. We apply our theory to DJA and FTSE indices.