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- Title
Detecting common breaks in the means of high dimensional cross-dependent panels.
- Authors
Horváth, Lajos; Liu, Zhenya; Rice, Gregory; Zhao, Yuqian
- Abstract
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.
- Subjects
CHANGE-point problems; MONTE Carlo method; PANEL analysis; TIME series analysis
- Publication
Econometrics Journal, 2022, Vol 25, Issue 2, p362
- ISSN
1368-4221
- Publication type
Article
- DOI
10.1093/ectj/utab028