Validating linear restrictions in linear regression models with general error structure - Hajo Holzmann; Aleksey Min; Claudia Czado (Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen, 2006)
A new method for testing linear restrictions in linear regression models is suggested. It allows to validate the linear restriction, up to a specified approximation error and with a specified error probability. The test relies on asymptotic normality of the test statistic, and therefore normality of the errors in the regression model is not required. In a simulation study the performance of the suggested method for model selection purposes, as compared to standard model selection criteria and the t-test, is examined. As an illustration we analyze the US college spending data from 1994.
urn:nbn:de:bvb:19-epub-1846-3
Keywords: 519; asymptotic normality; linear regression; model selection; model validation