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Variable Selection in Regression Models Using Global Sensitivity Analysis

Author

Listed:
  • Becker William
  • Paruolo Paolo

    (European Commission, Joint Research Centre, Ispra, VA, Italy)

  • Saltelli Andrea

    (Centre for the Study of the Sciences and the Humanities, University of Bergen, Bergen, Norway)

Abstract

Global sensitivity analysis is primarily used to investigate the effects of uncertainties in the input variables of physical models on the model output. This work investigates the use of global sensitivity analysis tools in the context of variable selection in regression models. Specifically, a global sensitivity measure is applied to a criterion of model fit, hence defining a ranking of regressors by importance; a testing sequence based on the ‘Pantula-principle’ is then applied to the corresponding nested submodels, obtaining a novel model-selection method. The approach is demonstrated on a growth regression case study, and on a number of simulation experiments, and it is found competitive with existing approaches to variable selection.

Suggested Citation

  • Becker William & Paruolo Paolo & Saltelli Andrea, 2021. "Variable Selection in Regression Models Using Global Sensitivity Analysis," Journal of Time Series Econometrics, De Gruyter, vol. 13(2), pages 187-233, July.
    • Handle: RePEc:bpj:jtsmet:v:13:y:2021:i:2:p:187-233:n:5
    DOI: 10.1515/jtse-2018-0025
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    References listed on IDEAS

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    Cited by:

    1. Maroli, John M., 2023. "Generating discrete dynamical system equations from input–output data using neural network identification models," Reliability Engineering and System Safety, Elsevier, vol. 235(C).

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    More about this item

    Keywords

    model selection; Monte Carlo; sensitivity analysis; simulation;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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