BOOSTING: WHY YOU CAN USE THE HP FILTER

Show simple item record

dc.contributor.author Phillips, Peter CB
dc.contributor.author Shi, Zhentao
dc.date.accessioned 2022-01-09T23:22:31Z
dc.date.available 2022-01-09T23:22:31Z
dc.date.issued 2020-12-27
dc.identifier.citation International Economic Review 62(2):521-570 27 Dec 2020
dc.identifier.issn 0020-6598
dc.identifier.uri https://hdl.handle.net/2292/57901
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.
dc.language en
dc.publisher Wiley
dc.relation.ispartofseries International Economic Review
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
dc.subject Social Sciences
dc.subject Economics
dc.subject Business & Economics
dc.subject HODRICK-PRESCOTT FILTER
dc.subject BUSINESS CYCLES
dc.subject UNIT-ROOT
dc.subject POWER TRANSFORMS
dc.subject SELECTION
dc.subject REGRESSION
dc.subject FREQUENCY
dc.subject INFERENCE
dc.subject MODELS
dc.subject 14 Economics
dc.title BOOSTING: WHY YOU CAN USE THE HP FILTER
dc.type Journal Article
dc.identifier.doi 10.1111/iere.12495
pubs.issue 2
pubs.begin-page 521
pubs.volume 62
dc.date.updated 2021-12-03T05:51:50Z
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
pubs.end-page 570
pubs.publication-status Published
dc.rights.accessrights http://purl.org/eprint/accessRights/RetrictedAccess en
pubs.subtype Article
pubs.subtype Journal
pubs.elements-id 833253
dc.identifier.eissn 1468-2354
pubs.online-publication-date 2020-12-27


Files in this item

Find Full text

This item appears in the following Collection(s)

Show simple item record

Share

Search ResearchSpace


Browse

Statistics