Detecting location shifts during model selection by step-indicator saturation

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 3 ; Year: 2015 ; Issue: 2 ; Pages: 240-264 ; Basel: MDPI

Classification and Topics

• Classification: Wirtschaft ; Model Construction and Estimation ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
• Subject: structural breaks ; model selection ; Monte Carlo ; indicator saturation ; Autometrics

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Castle, Jennifer L. ; Doornik, Jurgen A. ; Hendry, David F. ; Pretis, Felix
• Event: Veröffentlichung
• (who): MDPI
• (where): Basel
• (when): 2015

Further information

• DOI: doi:10.3390/econometrics3020240
• Handle: http://hdl.handle.net/10419/171826
• Last update: 10.03.2025, 11:41 AM CET