Deriving Tests of the Semi-Linear Regression Model Using the Density Function of a Maximal Invariant
AbstractIn 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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Bibliographic InfoPaper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 19/05.
Length: 12 pages
Date of creation: 2005
Date of revision:
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Postal: PO Box 11E, Monash University, Victoria 3800, Australia
Web page: http://www.buseco.monash.edu.au/depts/ebs/
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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 and Methodology: General - - - Hypothesis Testing: General
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- 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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