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Empirical Model Discovery and Theory EvaluationAutomatic Selection Methods in Econometrics$
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David F. Hendry and Jurgen A. Doornik

Print publication date: 2014

Print ISBN-13: 9780262028356

Published to MIT Press Scholarship Online: January 2015

DOI: 10.7551/mitpress/9780262028356.001.0001

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Detecting Outliers and Breaks Using IIS

Detecting Outliers and Breaks Using IIS

Chapter:
(p.175) 15 Detecting Outliers and Breaks Using IIS
Source:
Empirical Model Discovery and Theory Evaluation
Author(s):

David F. Hendry

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262028356.003.0015

The last of our six stages concerns detecting outliers and breaks. Impulse-indicator saturation (IIS: see Hendry et al., 2008, and Johansen and Nielsen, 2009) is analyzed under the null of no outliers, but with the aim of detecting and removing outliers and location shifts when they are present. The procedure creates an indicator for every observation, entered (in the simplest case) in blocks of T/2, noting that indicators are mutually orthogonal. First, add half the indicators, select as usual, record the outcome, then drop that set of indicators; next add the other half, selecting again. These first two steps correspond to dummying out T/2 observations for estimation. Now combine the significant indicators and select as usual. Overall, αT indicators will be retained on average by chance. Setting α≤ r/T (when r is small, e.g., unity) then maintains the average false null retention at r outliers, equivalent to losing r observations, which is a small efficiency loss when testing for breaks at T points. The theory generalizes to more, and unequal, splits, as well as to dynamic models, and is related to robust estimation. IIS also introduces selection when N ≥ T in its simplest setting.

Keywords:   Outliers, location shifts, impulse-indicator saturation, gauge, robustness

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