Automatic selection of indicators in a fully saturated regression

Publicação

dc.contributor.author	Santos, Carlos
dc.contributor.author	Hendry, David F.
dc.contributor.author	Johansen, Soren
dc.date.accessioned	2011-10-21T10:22:49Z
dc.date.available	2011-10-21T10:22:49Z
dc.date.issued	2008
dc.description.abstract	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	por
dc.identifier.citation	SANTOS, Carlos; HENDRY, David F.; JOHANSEN, Soren - Automatic selection of indicators in a fully saturated regression. Computational Statistics. ISSN: 1613-9658. Vol.23 (2008) p. 317–335	por
dc.identifier.doi	10.1007/s00180-007-0054-z
dc.identifier.uri	http://hdl.handle.net/10400.14/6624
dc.language.iso	eng	por
dc.peerreviewed	yes	por
dc.publisher	Springer	por
dc.subject	Indicators	por
dc.subject	Regression saturation	por
dc.subject	Subset selection	por
dc.subject	Model selection	por
dc.title	Automatic selection of indicators in a fully saturated regression	por
dc.type	journal article
dspace.entity.type	Publication
rcaap.rights	openAccess	por
rcaap.type	article	por

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