Detecting location shifts during model selection by step-indicator saturation - Jennifer L. Castle; Jurgen A. Doornik; David F. Hendry; Felix Pretis (Econometrics, 2015)
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.
Keywords: C51; C22; 330; structural breaks; model selection; Monte Carlo; indicator saturation; Autometrics