Chen, Likai, Wang, Weining and Wu, Wei Biao (2020) Dynamic Semiparametric Factor Model with Structural Breaks. Journal of Business and Economic Statistics. 1730857. ISSN 0735-0015
Abstract
For the change-point analysis of a high-dimensional time series, we consider a semiparametric model with dynamic structural break factors. With our model, the observations are described by a few low-dimensional factors with time-invariant loading functions of the covariates. Regarding the structural break, the factors are assumed to be nonstationary and follow a vector autoregression (VAR) process with a change in the parameter values. In addition, to account for the known spatial discrepancies, we introduce discrete loading functions. We study the theoretical properties of the estimates of the loading functions and the factors. Moreover, we provide both the consistency and the asymptotic normality for making an inference on the estimated breakpoint. {Importantly, our results hold for both large and small breaks in the factor dependency structure.} The estimation precision is further illustrated via a simulation study. Finally, we present two empirical applications in modeling the dynamics of the minimum wage policy in China and analyzing a limit order book dataset.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 American Statistical Association. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Social Sciences (York) > Economics and Related Studies (York) |
Depositing User: | Pure (York) |
Date Deposited: | 12 Feb 2020 17:00 |
Last Modified: | 17 Dec 2024 00:15 |
Published Version: | https://doi.org/10.1080/07350015.2020.1730857 |
Status: | Published online |
Refereed: | Yes |
Identification Number: | 10.1080/07350015.2020.1730857 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:157009 |
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