Quantifying the data-dredging bias in structural break tests

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Statistical Papers ; ISSN: 1613-9798 ; Volume: 63 ; Year: 2021 ; Issue: 1 ; Pages: 143-155 ; Berlin, Heidelberg: Springer

Classification and Topics

• Classification: Mathematik ; Hypothesis Testing: General ; Methodological Issues: General
• Subject: Data-dredging bias ; Hypothesis test ; Monitoring ; Structural breaks

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Hoga, Yannick
• Event: Veröffentlichung
• (who): Springer
• (where): Berlin, Heidelberg
• (when): 2021

Further information

• DOI: doi:10.1007/s00362-021-01233-4
• Last update: 10.03.2025, 11:42 AM CET