dc.contributor.author |
Phillips, Peter CB |
|
dc.contributor.author |
Shi, Zhentao |
|
dc.date.accessioned |
2022-01-09T23:22:31Z |
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dc.date.available |
2022-01-09T23:22:31Z |
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dc.date.issued |
2020-12-27 |
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dc.identifier.citation |
International Economic Review 62(2):521-570 27 Dec 2020 |
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dc.identifier.issn |
0020-6598 |
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dc.identifier.uri |
https://hdl.handle.net/2292/57901 |
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dc.description.abstract |
We propose a procedure of iterating the HP filter to produce a smarter smoothing device, called the boosted HP (bHP) filter, based on L2-boosting in machine learning. Limit theory shows that the bHP filter asymptotically recovers trend mechanisms that involve integrated processes, deterministic drifts, and structural breaks, covering the most common trends that appear in current modeling methodology. A stopping criterion automates the algorithm, giving a data-determined method for data-rich environments. The methodology is illustrated in simulations and with three real data examples that highlight the differences between simple HP filtering, the bHP filter, and an alternative autoregressive approach. |
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dc.language |
en |
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dc.publisher |
Wiley |
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dc.relation.ispartofseries |
International Economic Review |
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dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
|
dc.rights |
This is the peer reviewed version of the following article: International Economic Review 62(2):521-570 27 Dec 2020, which has been published in final form at http://doi.org/10.1111/iere.12495 This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited. |
|
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
|
dc.rights.uri |
https://authorservices.wiley.com/author-resources/Journal-Authors/licensing/self-archiving.html |
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dc.subject |
Social Sciences |
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dc.subject |
Economics |
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dc.subject |
Business & Economics |
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dc.subject |
HODRICK-PRESCOTT FILTER |
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dc.subject |
BUSINESS CYCLES |
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dc.subject |
UNIT-ROOT |
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dc.subject |
POWER TRANSFORMS |
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dc.subject |
SELECTION |
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dc.subject |
REGRESSION |
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dc.subject |
FREQUENCY |
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dc.subject |
INFERENCE |
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dc.subject |
MODELS |
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dc.subject |
14 Economics |
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dc.title |
BOOSTING: WHY YOU CAN USE THE HP FILTER |
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dc.type |
Journal Article |
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dc.identifier.doi |
10.1111/iere.12495 |
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pubs.issue |
2 |
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pubs.begin-page |
521 |
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pubs.volume |
62 |
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dc.date.updated |
2021-12-03T05:51:50Z |
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dc.rights.holder |
Copyright: Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association |
en |
pubs.author-url |
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000602525200001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=6e41486220adb198d0efde5a3b153e7d |
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pubs.end-page |
570 |
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pubs.publication-status |
Published |
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dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |
pubs.subtype |
Article |
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pubs.subtype |
Journal |
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pubs.elements-id |
833253 |
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dc.identifier.eissn |
1468-2354 |
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pubs.online-publication-date |
2020-12-27 |
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