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

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Author Info
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/depts/ebs/pubs/wpapers/2005/wp19-05.pdf
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Publisher 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
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Handle: RePEc:msh:ebswps:2005-19

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

Find related papers by JEL classification:
C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing

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References listed on IDEAS
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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 1994-6, Monash University, Department of Econometrics and Business Statistics.
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