Global search regression: A new automatic model-selection technique for cross-section, time-series, and panel-data regressions
Pablo Gluzmann () and
Stata Journal, 2015, vol. 15, issue 2
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.
Keywords: Research; Methods/; Statistical; Methods (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2) Track citations by RSS feed
Downloads: (external link)
Journal Article: Global search regression: A new automatic model-selection technique for cross-section, time-series, and panel-data regressions (2015)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ags:stataj:275931
Access Statistics for this article
More articles in Stata Journal from StataCorp LP
Bibliographic data for series maintained by AgEcon Search ().