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Title:Validating linear restrictions in linear regression models with general error structure PDF Logo
Authors:Holzmann, Hajo
Min, Aleksey
Czado, Claudia
Issue Date:2006
Series/Report no.:Discussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 478
Abstract: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.
Subjects:asymptotic normality
linear regression
model selection
model validation
Persistent Identifier of the first edition:urn:nbn:de:bvb:19-epub-1846-3
Document Type:Working Paper
Appears in Collections:Discussion papers, SFB 386, LMU München

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