Filtered Log-periodogram Regression of long memory processes
AbstractFiltered log-periodogram regression estimation of the fractional differencing parameter d is considered. Asymptotic properties are derived and the effect of filtering on ˆ d is investigated. It is shown that the estimator by Geweke and Porter-Hudak (1983) can be improved significantly using a simple family of filters. The essential improvement is based on a binary decision that is asymptotically correct with probability one. The idea is closely related to the well known technique of pre-whitening.
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Bibliographic InfoPaper provided by Center of Finance and Econometrics, University of Konstanz in its series CoFE Discussion Paper with number 08-10.
Length: 21 pages
Date of creation: 01 Nov 2008
Date of revision:
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-12-21 (All new papers)
- NEP-ECM-2008-12-21 (Econometrics)
- NEP-ETS-2008-12-21 (Econometric Time Series)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Katsumi Shimotsu & Peter C.B. Phillips, 2000. "Pooled Log Periodogram Regression," Cowles Foundation Discussion Papers 1267, Cowles Foundation for Research in Economics, Yale University.
- Velasco, Carlos, .
"Non-stationary log-periodogram regression,"
Open Access publications from Universidad Carlos III de Madrid
info:hdl:10016/4554, Universidad Carlos III de Madrid.
- Velasco, Carlos, . "Non-stationary log-periodogram regression," Open Access publications from Universidad Carlos III de Madrid info:hdl:10016/4346, Universidad Carlos III de Madrid.
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