Detecting Location Shifts during Model Selection by Step-Indicator Saturation
By Jennifer L. Castle, Jurgen A. Doornik, David F. Hendry, F. Pretis. 2015
153 citations recorded by the source corpus.
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.
Subfields: Control and Systems Engineering, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/21b68bc92f845f3b9d38d819662fe2266fe86d0c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Control and Systems Engineering
- F. Pretis
- Georgios Sermpinis
- Christoph Bergmeir
- Nikolaos Kourentzes
- Pierre Pinson
- Konstantinos Nikolopoulos
- David F. Hendry
- Devon K. Barrow
In Statistics and Probability
- Konstantinos Nikolopoulos
- Vassilios Assimakopoulos
- Nikolaos Kourentzes
- Fotios Petropoulos
- Evangelos Spiliotis
- Spyros Makridakis
- Christoph Bergmeir
- Stephan Kolassa
Citations in the corpus
- Does a Carbon Tax Reduce CO2 Emissions? Evidence from British Columbia (2019, methodology): “1 3 (Castle et al. 2015 implemented in R using `gets’, see Pretis et al. 2018a, b), though the LASSO and related estimators are also feasible. Using a general-to-specific indicator saturation approach, the general model (2) is reduced to a specific one (removing treatment dummy variables) using auto”
- Forecasting: theory and practice (2020, methodology): “The multi-step forecasts over 2012—2017 from a VAR in panel (d) of figure 12 show the advantage of using step-indicator saturation (SIS: Castle et al., 2015c).”