Robust Testing for Explosive Behavior with Strongly Dependent Errors

Yiu Lim Lui, Jun Yu and Peter Phillips
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Yiu Lim Lui: Dongbei University of Finance and Economics

No 2350, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: 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 procedures in practical work.

Pages: 68 pages
Date: 2022-10
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Robust testing for explosive behavior with strongly dependent errors (2024)
Working Paper: Robust Testing for Explosive Behavior with Strongly Dependent Errors (2022)
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