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Testing for structural breaks in the presence of data perturbations: impacts and wavelet-based improvements

In English

Författare

Summary, in English

This paper investigates how classical measurement error and additive outliers (AO) influence tests for structural change based on F-statistics. We derive theoretically the impact of general additive disturbances in the regressors on the asymptotic distribution of these tests for structural change. The small sample properties in the case of classical measurement error and AO are investigated via Monte Carlo simulations, revealing that sizes are biased upwards and that powers are reduced. Two-wavelet-based denoising methods are used to reduce these distortions. We show that these two methods can significantly improve the performance of structural break tests.

Publiceringsår

2015

Språk

Engelska

Sidor

3468-3479

Publikation/Tidskrift/Serie

Journal of Statistical Computation and Simulation

Volym

85

Issue

17

Dokumenttyp

Artikel i tidskrift

Förlag

Taylor & Francis

Ämne

  • Economics

Nyckelord

  • structural breaks
  • measurement error
  • additive outlier
  • wavelet transform
  • empirical Bayes thresholding

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 1563-5163