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

Reese, Simon LU and Li, Yushu (2015) In Journal of Statistical Computation and Simulation 85(17). p.3468-3479
Abstract
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.
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
structural breaks, measurement error, additive outlier, wavelet transform, empirical Bayes thresholding
in
Journal of Statistical Computation and Simulation
volume
85
issue
17
pages
3468 - 3479
publisher
Taylor & Francis
external identifiers
  • scopus:84941743297
  • wos:000371315300005
ISSN
1563-5163
DOI
10.1080/00949655.2014.979824
language
English
LU publication?
yes
id
6196d274-ff93-4cfa-8af0-82d5488f7478 (old id 4905262)
alternative location
http://www.tandfonline.com/doi/full/10.1080/00949655.2014.979824
date added to LUP
2015-01-12 11:34:38
date last changed
2017-05-07 03:02:13
@article{6196d274-ff93-4cfa-8af0-82d5488f7478,
  abstract     = {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.},
  author       = {Reese, Simon and Li, Yushu},
  issn         = {1563-5163},
  keyword      = {structural breaks,measurement error,additive outlier,wavelet transform,empirical Bayes thresholding},
  language     = {eng},
  number       = {17},
  pages        = {3468--3479},
  publisher    = {Taylor & Francis},
  series       = {Journal of Statistical Computation and Simulation},
  title        = {Testing for structural breaks in the presence of data perturbations: impacts and wavelet-based improvements},
  url          = {http://dx.doi.org/10.1080/00949655.2014.979824},
  volume       = {85},
  year         = {2015},
}