Automatic selection of indicators in a fully saturated regression
We consider selecting a regression model, using a variant of the generalto- specific algorithm in PcGets, when there are more variables than observations. We look at the special case where the variables are single impulse dummies, one defined for each observation. We show that this setting is unprob...
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| Outros Autores: | , |
| Formato: | article |
| Idioma: | eng |
| Publicado em: |
2011
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| Assuntos: | |
| Texto completo: | http://hdl.handle.net/10400.14/6624 |
| País: | Portugal |
| Oai: | oai:repositorio.ucp.pt:10400.14/6624 |
| Resumo: | We consider selecting a regression model, using a variant of the generalto- specific algorithm in PcGets, when there are more variables than observations. We look at the special case where the variables are single impulse dummies, one defined for each observation. We show that this setting is unproblematic if tackled appropriately, and obtain the asymptotic distribution of the mean and variance in a location-scale model, under the null that no impulses matter. Monte Carlo simulations confirm the null distributions and suggest extensions to highly non-normal cases |
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