Conditional variance estimation in regression models with long memory
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...
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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
14 March 2012
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| In: |
Journal of time series analysis
Year: 2012, Volume: 33, Issue: 3, Pages: 468-483 |
| ISSN: | 1467-9892 |
| DOI: | 10.1111/j.1467-9892.2012.00782.x |
| Online Access: | Verlag, Volltext: http://dx.doi.org/10.1111/j.1467-9892.2012.00782.x Verlag, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-9892.2012.00782.x 
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| Author Notes: | Rafal Kulik and Cornelia Wichelhaus |
MARC
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| 520 | |a 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. | ||
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