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Global search regression: A new automatic model-selection technique for cross-section, time-series, and panel-data regressions


  • Pablo Gluzmann

    () (Argentine National Council of Scientific and Technological Research)

  • Demian Panigo

    () (Argentine National Council of Scientific and Technological Research)


In this article, we present gsreg, a new automatic model-selection technique for cross-section, time-series, and panel-data regressions. Like other exhaustive search algorithms (for example, vselect), gsreg avoids characteristic path-dependence traps of standard approaches as well as backward- and forwardlooking approaches (like PcGets or relevant transformation of the inputs network approach). However, gsreg is the first code that 1) guarantees optimality with out-of-sample selection criteria; 2) allows residual testing for each alternative; and 3) provides (depending on user specifications) a full-information dataset with outcome statistics for every alternative model. Copyright 2015 by StataCorp LP.

Suggested Citation

  • Pablo Gluzmann & Demian Panigo, 2015. "Global search regression: A new automatic model-selection technique for cross-section, time-series, and panel-data regressions," Stata Journal, StataCorp LP, vol. 15(2), pages 325-349, June.
  • Handle: RePEc:tsj:stataj:v:15:y:2015:i:2:p:325-349
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    Cited by:

    1. Stronzik, Marcus & Zuloaga, Gonzalo, 2020. "Empirische Untersuchung der FTTB/H-Ausbauaktivität im europäischen Vergleich," WIK Discussion Papers 465, WIK Wissenschaftliches Institut für Infrastruktur und Kommunikationsdienste GmbH.
    2. Steinbuks, Jevgenijs, 2019. "Assessing the accuracy of electricity production forecasts in developing countries," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1175-1185.
    3. Gluzmann, Pablo & Guzman, Martin, 2017. "Assessing the robustness of the relationship between financial reforms and banking crises," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 49(C), pages 32-47.


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