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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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Impulse-Indicator Saturation for Multiple Breaks

Impulse-Indicator Saturation for Multiple Breaks

(p.243) 20 Impulse-indicator Saturation for Multiple Breaks
Empirical Model Discovery and Theory Evaluation

David F. Hendry

The MIT Press

Chapter 15 considered the theory and practice of IIS, to show that the cost of applying that approach under the null of no outliers or breaks was low at reasonably tight significance levels. A pilot Monte Carlo illustrated its ability to detect outliers and breaks. Chapter 19 discussed the algorithm in Autometrics for implementing IIS together with selecting variables. Since the objective of IIS is to detect and help model breaks, in this chapter, we extend the range of experiments from D1–D3 considered earlier. A variety of breaks is examined, from a single start or end of sample location shift, through blocks of five 4-period shifts to 20 location shifts spread across a sample of T = 100. When there is more than a single break, a failure to detect one increases the residual variance and so lowers the probability of detecting any others. Consequently, potency is low for small shifts, but rises quickly with the break magnitude, even in dynamic models.

Keywords:   Multiple breaks, impulse-indicator saturation, potency

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