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

Structural breaks in panel data: large number of panels and short length time series

Lajos Horváth, Zhenya Liu, Gregory Rice and Yuqian Zhao

The Econometrics Journal, 2022, vol. 25, issue 2, 362-383

Abstract: SummaryThe 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 , 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.

Keywords: High dimensional panel; change points in mean; asymptotic limit; Monte Carlo simulation; FRED-MD macroeconomic data (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:oup:emjrnl:v:25:y:2022:i:2:p:362-383.

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