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BOOTSTRAP INFERENCE FOR MULTIPLE CHANGE-POINTS IN TIME SERIES
In this paper, we propose two bootstrap procedures, namely parametric and block bootstrap, to approximate the finite sample distribution of change-point estimators for piecewise stationary time series. The bootstrap procedures are then used to develop a generalized likelihood ratio scan method (GLRS...
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| Published in: | Econometric theory 2022-08, Vol.38 (4), p.752-792 |
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| Main Authors: | , , |
| Format: | Article |
| Language: | English |
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| container_end_page | 792 |
| container_issue | 4 |
| container_start_page | 752 |
| container_title | Econometric theory |
| container_volume | 38 |
| creator | Ng, Wai Leong Pan, Shenyi Yau, Chun Yip |
| description | In this paper, we propose two bootstrap procedures, namely parametric and block bootstrap, to approximate the finite sample distribution of change-point estimators for piecewise stationary time series. The bootstrap procedures are then used to develop a generalized likelihood ratio scan method (GLRSM) for multiple change-point inference in piecewise stationary time series, which estimates the number and locations of change-points and provides a confidence interval for each change-point. The computational complexity of using GLRSM for multiple change-point detection is as low as
$O(n(\log n)^{3})$
for a series of length n. Extensive simulation studies are provided to demonstrate the effectiveness of the proposed methodology under different scenarios. Applications to financial time series are also illustrated. |
| doi_str_mv | 10.1017/S0266466621000293 |
| format | article |
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$O(n(\log n)^{3})$
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$O(n(\log n)^{3})$
for a series of length n. Extensive simulation studies are provided to demonstrate the effectiveness of the proposed methodology under different scenarios. Applications to financial time series are also illustrated.</description><subject>Approximation</subject><subject>Changes</subject><subject>Econometrics</subject><subject>Economic theory</subject><subject>Genetic algorithms</subject><subject>Random variables</subject><subject>Simulation</subject><subject>Time series</subject><issn>0266-4666</issn><issn>1469-4360</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>8BJ</sourceid><sourceid>M0C</sourceid><recordid>eNp1kE9Pg0AQxTdGE2v1A3gj8YzO_mG3e_BQydKSUCBAz2RZFtPG2rq0B7-9kDbxYDzMzOH93pvkIfSI4RkDFi8lEM4Z55xgACCSXqEJZlz6jHK4RpNR9kf9Ft31_RYAEynoBL2-ZVlVVsU89-I0UoVKQ-VFWeGt1kkV54nywuU8XSg_z-K0KgfIq-KV8kpVxKq8Rzed_ujtw-VO0TpSVbj0k2wRh_PENxSCo8-ltRIbpmVr8DCWBMYQHDSc24Aa3rVgWpCNBB3YThgjZavJTAxbaMZndIqezrkHt_862f5Yb_cn9zm8rIkAMWNCUDpQ-EwZt-97Z7v64DY77b5rDPXYUv2npcFDLx69a9ymfbe_0f-7fgBYx2L7</recordid><startdate>20220801</startdate><enddate>20220801</enddate><creator>Ng, Wai Leong</creator><creator>Pan, Shenyi</creator><creator>Yau, Chun Yip</creator><general>Cambridge University Press</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>8BJ</scope><scope>8FK</scope><scope>8FL</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FQK</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>JBE</scope><scope>K60</scope><scope>K6~</scope><scope>L.-</scope><scope>M0C</scope><scope>M2O</scope><scope>MBDVC</scope><scope>PADUT</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYYUZ</scope><scope>Q9U</scope></search><sort><creationdate>20220801</creationdate><title>BOOTSTRAP INFERENCE FOR MULTIPLE CHANGE-POINTS IN TIME SERIES</title><author>Ng, Wai Leong ; Pan, Shenyi ; Yau, Chun Yip</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c305t-69ee91c4a9dc19dce25cc215b66e53c6fd0cd09b90a5ef7cc99da2879da7a4683</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Approximation</topic><topic>Changes</topic><topic>Econometrics</topic><topic>Economic theory</topic><topic>Genetic algorithms</topic><topic>Random variables</topic><topic>Simulation</topic><topic>Time series</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ng, Wai Leong</creatorcontrib><creatorcontrib>Pan, Shenyi</creatorcontrib><creatorcontrib>Yau, Chun Yip</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Business Premium Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>International Bibliography of the Social Sciences</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>International Bibliography of the Social Sciences</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Global</collection><collection>ProQuest research library</collection><collection>Research Library (Corporate)</collection><collection>Research Library China</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ABI/INFORM Collection China</collection><collection>ProQuest Central Basic</collection><jtitle>Econometric theory</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ng, Wai Leong</au><au>Pan, Shenyi</au><au>Yau, Chun Yip</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>BOOTSTRAP INFERENCE FOR MULTIPLE CHANGE-POINTS IN TIME SERIES</atitle><jtitle>Econometric theory</jtitle><addtitle>Econom. Theory</addtitle><date>2022-08-01</date><risdate>2022</risdate><volume>38</volume><issue>4</issue><spage>752</spage><epage>792</epage><pages>752-792</pages><issn>0266-4666</issn><eissn>1469-4360</eissn><abstract>In this paper, we propose two bootstrap procedures, namely parametric and block bootstrap, to approximate the finite sample distribution of change-point estimators for piecewise stationary time series. The bootstrap procedures are then used to develop a generalized likelihood ratio scan method (GLRSM) for multiple change-point inference in piecewise stationary time series, which estimates the number and locations of change-points and provides a confidence interval for each change-point. The computational complexity of using GLRSM for multiple change-point detection is as low as
$O(n(\log n)^{3})$
for a series of length n. Extensive simulation studies are provided to demonstrate the effectiveness of the proposed methodology under different scenarios. Applications to financial time series are also illustrated.</abstract><cop>New York, USA</cop><pub>Cambridge University Press</pub><doi>10.1017/S0266466621000293</doi><tpages>41</tpages></addata></record> |
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| ispartof | Econometric theory, 2022-08, Vol.38 (4), p.752-792 |
| issn | 0266-4666 1469-4360 |
| language | eng |
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| source | International Bibliography of the Social Sciences (IBSS); ABI/INFORM Global; Cambridge University Press |
| subjects | Approximation Changes Econometrics Economic theory Genetic algorithms Random variables Simulation Time series |
| title | BOOTSTRAP INFERENCE FOR MULTIPLE CHANGE-POINTS IN TIME SERIES |
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