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Econometrics 2015, 3(2), 240-264; doi:10.3390/econometrics3020240

Detecting Location Shifts during Model Selection by Step-Indicator Saturation

1
Magdalen College and Institute for New Economic Thinking, Oxford Martin School, Oxford University, Eagle House, Walton Well Road, Oxford OX2 6ED, UK
2
Economics Department and Institute for New Economic Thinking, Oxford Martin School, Oxford University, Eagle House, Walton Well Road, Oxford OX2 6ED, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Kerry Patterson
Received: 16 February 2015 / Accepted: 26 March 2015 / Published: 14 April 2015
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Abstract

To capture location shifts in the context of model selection, we propose selecting significant step indicators from a saturating set added to the union of all of the candidate variables. The null retention frequency and approximate non-centrality of a selection test are derived using a ‘split-half’ analysis, the simplest specialization of a multiple-path block-search algorithm. Monte Carlo simulations, extended to sequential reduction, confirm the accuracy of nominal significance levels under the null and show retentions when location shifts occur, improving the non-null retention frequency compared to the corresponding impulse-indicator saturation (IIS)-based method and the lasso. View Full-Text
Keywords: structural breaks; model selection; Monte Carlo; indicator saturation; Autometrics structural breaks; model selection; Monte Carlo; indicator saturation; Autometrics
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Castle, J.L.; Doornik, J.A.; Hendry, D.F.; Pretis, F. Detecting Location Shifts during Model Selection by Step-Indicator Saturation. Econometrics 2015, 3, 240-264.

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