Global search regression: a new automatic model-selection technique for cross-section, time-series, and panel-data regressions
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 f...
Guardado en:
| Autores principales: | , |
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| Formato: | Articulo |
| Lenguaje: | Inglés |
| Publicado: |
2015
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/123907 |
| Aporte de: |
| Sumario: | 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. |
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