Estimating the mean under strong persistence
Uwe Hassler and
Mehdi Hosseinkouchack ()
Economics Letters, 2020, vol. 188, issue C
We study a maximum likelihood [ML] type estimator for the mean of strongly persistent processes. Its limiting Gaussian distribution is obtained and compared with that of the arithmetic sample mean. The rates of convergence turn out to be equal. Two special cases of strong persistence are discussed: Fractional integration [FI] and harmonic weighting [HW]. Notwithstanding equal rates, efficiency gains relative to the arithmetic mean are available under FI, while for HW processes the relative efficiency turns out to be one asymptotically. For applied work, where the true model is not known, we suggest to use the estimator building on HW as a general purpose device, since it does not require the estimation of any parameter.
Keywords: Limiting normality; Long memory; Fractional integration; Harmonic weighting; Efficiency (search for similar items in EconPapers)
JEL-codes: C13 C22 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
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:eee:ecolet:v:188:y:2020:i:c:s0165176520300069
Access Statistics for this article
Economics Letters is currently edited by Economics Letters Editorial Office
More articles in Economics Letters from Elsevier
Bibliographic data for series maintained by Nithya Sathishkumar ().