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Deriving Tests of the Semi-Linear Regression Model Using the Density Function of a Maximal Invariant

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  • Jahar L. Bhowmik

    ()

  • Maxwell L. King

    ()

Abstract

In the context of a general regression model in which some regression coefficients are of interest and others are purely nuisance parameters, we derive the density function of a maximal invariant statistic with the aim of testing for the inclusion of regressors (either linear or non-linear) in linear or semi-linear models. This allows the construction of the locally best invariant test, which in two important cases is equivalent to the one-sided t-test for a regression coefficient in an artificial linear regression model.

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File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2005/wp19-05.pdf
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Bibliographic Info

Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 19/05.

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Length: 12 pages
Date of creation: 2005
Date of revision:
Handle: RePEc:msh:ebswps:2005-19

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Keywords: Invariance; linear regression model; locally best invariant test; non-linear regression model; nuisance parameters; t-test.;

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  1. Wu, P.X. & King, M.L., 1994. "One Sided Hypothesis Testing in Econometrics: A Survey," Monash Econometrics and Business Statistics Working Papers 6/94, Monash University, Department of Econometrics and Business Statistics.
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