Detecting common breaks in the means of high dimensional cross-dependent panels

Lajos Horvath, Zhenya Liu, Gregory Rice and Yuqian Zhao
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Zhenya Liu: CERGAM - Centre d'Études et de Recherche en Gestion d'Aix-Marseille - AMU - Aix Marseille Université - UTLN - Université de Toulon

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Abstract: Summary The problem of detecting change points in the mean of high dimensional panel data with potentially strong cross-sectional dependence is considered. Under the assumption that the cross-sectional dependence is captured by an unknown number of common factors, a new CUSUM-type statistic is proposed. We derive its asymptotic properties under three scenarios depending on to what extent the common factors are asymptotically dominant. With panel data consisting of N cross sectional time series of length T, the asymptotic results hold under the mild assumption that $\min \lbrace N,T\rbrace \rightarrow \infty$, with an otherwise arbitrary relationship between N and T, allowing the results to apply to most panel data examples. Bootstrap procedures are proposed to approximate the sampling distribution of the test statistics. A Monte Carlo simulation study showed that our test outperforms several other existing tests in finite samples in a number of cases, particularly when N is much larger than T. The practical application of the proposed results are demonstrated with real data applications to detecting and estimating change points in the high dimensional FRED-MD macroeconomic data set.

Date: 2021-09-04
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Published in Econometrics Journal, 2021, ⟨10.1093/ectj/utab028⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03511434

DOI: 10.1093/ectj/utab028

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