Estimation of long memory parameter in nonparametric regression

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This paper considers the estimation of the long memory parameter in nonparametric regression with strongly correlated errors. The key idea is to minimize a unified mean squared error of long memory parameter to select both kernel bandwidth and the number of frequencies used in exact local Whittle estimation. A unified mean squared error framework is more natural because it provides both goodness of fit and measure of strong dependence. The block bootstrap is applied to evaluate the mean squared error. Finite sample performance using Monte Carlo simulations shows the closest performance to the oracle. The proposed method outperforms existing methods especially when dependency and sample size increase. The proposed method is also illustreated to the volatility of exchange rate between Korean Won for US dollar.
Publisher
KOREAN STATISTICAL SOC
Issue Date
2019-11
Language
English
Article Type
Article
Citation

COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS, v.26, no.6, pp.611 - 622

ISSN
2287-7843
DOI
10.29220/CSAM.2019.26.6.611
URI
http://hdl.handle.net/10203/270708
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