Hualde, Javier and Iacone, Fabrizio orcid.org/0000-0002-2681-9036 (2017) Fixed bandwidth asymptotics for the studentized mean of fractionally integrated processes. Economics Letters. pp. 39-43. ISSN 0165-1765
Abstract
We consider inference for the mean of a general stationary process based on standardizing the sample mean by a frequency domain estimator of the long run variance. Here, the main novelty is that we consider alternative asymptotics in which the bandwidth is kept fixed. This does not yield a consistent estimator of the long run variance, but, for the weakly dependent case, the studentized sample mean has a Student-t limit distribution, which, for any given bandwidth, appears to be more precise than the traditional Gaussian limit. When data are fractionally integrated, the fixed bandwidth limit distribution of the studentized mean is not standard, and we derive critical values for various bandwidths. By a Monte Carlo experiment of finite sample performance we find that this asymptotic result provides a better approximation than other proposals like the test statistic based on the Memory Autocorrelation Consistent (MAC) estimator of the variance of the sample mean.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 Elsevier B.V.Allrightsreserved. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. |
Keywords: | Fractional integration,Large-m and fixed-m asymptotic theory,Long run variance estimation |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Social Sciences (York) > Economics and Related Studies (York) |
Depositing User: | Pure (York) |
Date Deposited: | 21 Nov 2016 16:02 |
Last Modified: | 24 Oct 2024 23:55 |
Published Version: | https://doi.org/10.1016/j.econlet.2016.10.014 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1016/j.econlet.2016.10.014 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:108056 |
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