Computational Statistics

, Volume 23, Issue 2, pp 317–335 | Cite as

Automatic selection of indicators in a fully saturated regression

Original Paper

Abstract

We consider selecting a regression model, using a variant of the general-to-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.

Keywords

Indicators Regression saturation Subset selection Model selection 

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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Carlos Santos
    • 1
  • David F. Hendry
    • 2
  • Soren Johansen
    • 3
  1. 1.Department of Economics and ManagementPortuguese Catholic UniversityPortoPortugal
  2. 2.Department of EconomicsUniversity of OxfordOxfordUK
  3. 3.Department of StatisticsUniversity of CopenhagenCopenhagenDenmark

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