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Published online by Cambridge University Press:  25 March 2022

Zhonghao Fu*
Fudan University and Shanghai Institute of International Finance and Economics
Yongmiao Hong*
Chinese Academy of Sciences and University of Chinese Academy of Sciences
Xia Wang*
Renmin University of China
Address correspondence to Zhonghao Fu, School of Economics, Fudan University, Shanghai, China; e-mail:; Yongmiao Hong, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China; e-mail:; Xia Wang, School of Economics, Renmin University of China, Beijing, China; e-mail:
Address correspondence to Zhonghao Fu, School of Economics, Fudan University, Shanghai, China; e-mail:; Yongmiao Hong, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China; e-mail:; Xia Wang, School of Economics, Renmin University of China, Beijing, China; e-mail:
Address correspondence to Zhonghao Fu, School of Economics, Fudan University, Shanghai, China; e-mail:; Yongmiao Hong, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China; e-mail:; Xia Wang, School of Economics, Renmin University of China, Beijing, China; e-mail:


We estimate and test for multiple structural breaks in distribution via an empirical characteristic function approach. By minimizing the sum of squared generalized residuals, we can consistently estimate the break fractions. We propose a sup-F type test for structural breaks in distribution as well as an information criterion and a sequential testing procedure to determine the number of breaks. We further construct a class of derivative tests to gauge possible sources of structural breaks, which is asymptotically more powerful than the smoothed nonparametric tests for structural breaks. Simulation studies show that our method performs well in determining the appropriate number of breaks and estimating the unknown breaks. Furthermore, the proposed tests have reasonable size and excellent power in finite samples. In an application to exchange rate returns, our tests are able to detect structural breaks in distribution and locate the break dates. Our tests also indicate that the documented breaks appear to occur in variance and higher-order moments, but not so often in mean.

© The Author(s) 2022. Published by Cambridge University Press

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Fu acknowledges financial support from the National Science Foundation of China (NSFC) (Nos. 71903032 and 72121002), Hong acknowledges financial support from the NSFC’s Fundamental Scientific Center Project (No. 71988101), and Wang acknowledges financial support from the NSFC (No. 71873151). We thank the Editor (Peter C.B. Phillips), the Co-Editor (Robert Taylor), three anonymous referees, and seminar participants in the 2019 City University of Hong Kong Workshop in Econometrics and Statistics, the International Workshop on Extreme Value Theory at Fudan University, and the 2019 Guangzhou Econometrics Workshop, for their insightful comments and suggestions. All remaining errors are solely ours.



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