Detecting Location Shifts during Model Selection by Step-Indicator Saturation Article Swipe

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Monte Carlo method Null hypothesis Null (SQL) Algorithm Mathematics Impulse (physics) Statistics Selection (genetic algorithm) Saturation (graph theory) Computer science Data mining Artificial intelligence Physics Combinatorics Quantum mechanics

Jennifer L. Castle , Jurgen A. Doornik , David F. Hendry , Felix Pretis ·

YOU? · · 2015 · Open Access · · DOI: https://doi.org/10.3390/econometrics3020240 · OA: W2006757773

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.