On parameter estimation for locally stationary long-memory processes
No 07/13, CoFE Discussion Papers from University of Konstanz, Center of Finance and Econometrics (CoFE)
We consider parameter estimation for time-dependent locally stationary long-memory processes. The asymptotic distribution of an estimator based on the local infinite autoregressive representation is derived, and asymptotic formulas for the mean squared error of the estimator, and the asymptotically optimal bandwidth are obtained. In spite of long memory, the optimal bandwidth turns out to be of the n-1/5 and inversely proportional to the square of the second derivative of d. In this sense, local estimation of d is comparable to regression smoothing with iid residuals.
Keywords: long memory; fractional ARIMA process; local stationarity; bandwidth selection (search for similar items in EconPapers)
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:zbw:cofedp:0713
Access Statistics for this paper
More papers in CoFE Discussion Papers from University of Konstanz, Center of Finance and Econometrics (CoFE) Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().