EconPapers    
Economics at your fingertips  
 

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

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.

Keywords: Research; Methods/; Statistical; Methods (search for similar items in EconPapers)
Date: 2015
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)
http://ageconsearch.umn.edu/record/275931/files/sjart_st0383.pdf (application/pdf)

Related works:
Journal Article: Global search regression: A new automatic model-selection technique for cross-section, time-series, and panel-data regressions (2015) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ags:stataj:275931

DOI: 10.22004/ag.econ.275931

Access Statistics for this article

More articles in Stata Journal from StataCorp LP
Bibliographic data for series maintained by AgEcon Search ().

 
Page updated 2020-05-07
Handle: RePEc:ags:stataj:275931