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

Pablo Gluzmann and Demian Panigo

Stata Journal, 2015, vol. 15, issue 2, 325-349

Abstract: 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.

Keywords: gsreg; automatic model selection; vselect; PcGets; RETINA (search for similar items in EconPapers)
Date: 2015
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