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

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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.

Detaljer

Författare
Enheter & grupper
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Nationalekonomi

Nyckelord

Originalspråkengelska
Sidor (från-till)3468-3479
TidskriftJournal of Statistical Computation and Simulation
Volym85
Utgåva nummer17
StatusPublished - 2015
PublikationskategoriForskning
Peer review utfördJa