Robust testing for explosive behavior with strongly dependent errors

A heteroskedasticity-autocorrelation robust (HAR) test statistic is proposed to test for the presence of explosive roots in financial or real asset prices when the equation errors are strongly dependent. Limit theory for the test statistic is developed and extended to heteroskedastic models. The new...

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Main Authors: LUI, Yiu Lim, PHILLIPS, Peter C. B., YU, Jun
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Language:English
Published: Institutional Knowledge at Singapore Management University 2022
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Online Access:https://ink.library.smu.edu.sg/soe_research/2631
https://ink.library.smu.edu.sg/context/soe_research/article/3630/viewcontent/TestStrongDependence23_A5_jun_allen.pdf
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spelling sg-smu-ink.soe_research-36302024-07-04T01:47:30Z Robust testing for explosive behavior with strongly dependent errors LUI, Yiu Lim PHILLIPS, Peter C. B. YU, Jun A heteroskedasticity-autocorrelation robust (HAR) test statistic is proposed to test for the presence of explosive roots in financial or real asset prices when the equation errors are strongly dependent. Limit theory for the test statistic is developed and extended to heteroskedastic models. The new test has stable size properties unlike conventional test statistics that typically lead to size distortion and inconsistency in the presence of strongly dependent equation errors. The new procedure can be used to consistently time-stamp the origination and termination of an explosive episode under similar conditions of long memory errors. Simulations are conducted to assess the finite sample performance of the proposed test and estimators. An empirical application to the S&P 500 index highlights the usefulness of the proposed proceduresin practical work. 2022-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/2631 https://ink.library.smu.edu.sg/context/soe_research/article/3630/viewcontent/TestStrongDependence23_A5_jun_allen.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University HAR test Long memory Explosiveness Unit root test S&P 500 Econometrics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic HAR test
Long memory
Explosiveness
Unit root test
S&P 500
Econometrics
spellingShingle HAR test
Long memory
Explosiveness
Unit root test
S&P 500
Econometrics
LUI, Yiu Lim
PHILLIPS, Peter C. B.
YU, Jun
Robust testing for explosive behavior with strongly dependent errors
description A heteroskedasticity-autocorrelation robust (HAR) test statistic is proposed to test for the presence of explosive roots in financial or real asset prices when the equation errors are strongly dependent. Limit theory for the test statistic is developed and extended to heteroskedastic models. The new test has stable size properties unlike conventional test statistics that typically lead to size distortion and inconsistency in the presence of strongly dependent equation errors. The new procedure can be used to consistently time-stamp the origination and termination of an explosive episode under similar conditions of long memory errors. Simulations are conducted to assess the finite sample performance of the proposed test and estimators. An empirical application to the S&P 500 index highlights the usefulness of the proposed proceduresin practical work.
format text
author LUI, Yiu Lim
PHILLIPS, Peter C. B.
YU, Jun
author_facet LUI, Yiu Lim
PHILLIPS, Peter C. B.
YU, Jun
author_sort LUI, Yiu Lim
title Robust testing for explosive behavior with strongly dependent errors
title_short Robust testing for explosive behavior with strongly dependent errors
title_full Robust testing for explosive behavior with strongly dependent errors
title_fullStr Robust testing for explosive behavior with strongly dependent errors
title_full_unstemmed Robust testing for explosive behavior with strongly dependent errors
title_sort robust testing for explosive behavior with strongly dependent errors
publisher Institutional Knowledge at Singapore Management University
publishDate 2022
url https://ink.library.smu.edu.sg/soe_research/2631
https://ink.library.smu.edu.sg/context/soe_research/article/3630/viewcontent/TestStrongDependence23_A5_jun_allen.pdf
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