Detecting Location Shifts during Model Selection by Step-Indicator Saturation
Jennifer L. Castle,
Jurgen A. Doornik,
David F. Hendry,
Felix Pretis
Affiliations
Jennifer L. Castle
Magdalen College and Institute for New Economic Thinking, Oxford Martin School, Oxford University, Eagle House, Walton Well Road, Oxford OX2 6ED, UK
Jurgen A. Doornik
Economics Department and Institute for New Economic Thinking, Oxford Martin School, Oxford University, Eagle House, Walton Well Road, Oxford OX2 6ED, UK
David F. Hendry
Economics Department and Institute for New Economic Thinking, Oxford Martin School, Oxford University, Eagle House, Walton Well Road, Oxford OX2 6ED, UK
Felix Pretis
Economics Department and Institute for New Economic Thinking, Oxford Martin School, Oxford University, Eagle House, Walton Well Road, Oxford OX2 6ED, UK
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.
