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A CROSS-SECTIONAL METHOD FOR RIGHT-TAILED PANIC TESTS UNDER A MODERATELY LOCAL TO UNITY FRAMEWORK

Published online by Cambridge University Press:  15 March 2022

Yohei Yamamoto*
Affiliation:
Hitotsubashi University
Tetsushi Horie
Affiliation:
Hitotsubashi University
*
Address correspondence to Yohei Yamamoto, Graduate School of Economics, Hitotsubashi University, 2-1 Naka, Kunitachi, Tokyo 186-8601, Japan; e-mail: [email protected].
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Abstract

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The left-tailed unit-root tests of the panel analysis of nonstationarity in idiosyncratic and common components (PANIC) proposed by Bai and Ng (2004, Econometrica 72, 1127–1177) have standard local asymptotic power. We assess the size and power properties of the right-tailed version of the PANIC tests when the common and/or the idiosyncratic components are moderately explosive. We find that, when an idiosyncratic component is moderately explosive, the tests for the common components may have considerable size distortions, and those for an idiosyncratic component may suffer from the nonmonotonic power problem. We provide an analytic explanation under the moderately local to unity framework developed by Phillips and Magdalinos (2007, Journal of Econometrics 136, 115–130). We then propose a new cross-sectional (CS) approach to disentangle the common and idiosyncratic components in a relatively short explosive window. Our Monte Carlo simulations show that the CS approach is robust to the nonmonotonic power problem.

Type
ARTICLES
Copyright
© The Author(s), 2022. Published by Cambridge University Press

Footnotes

This is a revised version of a paper previously circulated under the title “Testing for Speculative Bubbles in Large Dimensional Financial Panel Data Sets.” Yamamoto acknowledges the financial support for this work from MEXT Grant-in-Aid for Scientific Research No. 13K03593. We would like to thank the Editor (Peter C.B. Phillips), the Co-Editor (Robert Taylor), and four anonymous referees for their valuable comments, in particular, an extra referee in the final round. We would also like to thank the seminar participants at the Japan-Korea Allied Conference in Econometrics, Hiroshima University, University of Lancaster, the Development Bank of Japan, Academia Sinica, Pi-Day Conference in honor of Professor Pierre Perron at Boston University, and SETA2019 for their useful comments. All remaining errors are our own.

References

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