Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/286808 
Authors: 
Year of Publication: 
2021
Citation: 
[Journal:] Statistical Papers [ISSN:] 1613-9798 [Volume:] 63 [Issue:] 1 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2021 [Pages:] 143-155
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
Structural break tests are often applied as a pre-step to ensure the validity of subsequent statistical analyses. Without any a priori knowledge of the type of breaks to expect, eye-balling the data can indicate changes in some parameter, e.g., the mean. This, however, can distort the result of a structural break test for that parameter, because the data themselves suggested the hypothesis. In this paper, we formalize the eye-balling procedure and theoretically derive the implied size distortion of the structural break test. We also show that eye-balling a stretch of historical data for possible changes in a parameter does not invalidate the subsequent procedure that monitors for structural change in new incoming observations. An empirical application to Bitcoin returns shows that taking into account the data-dredging bias, which is incurred by looking at the data, can lead to different test decisions.
Subjects: 
Data-dredging bias
Hypothesis test
Monitoring
Structural breaks
JEL: 
C12
C18
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
Document Version: 
Published Version

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