Maximal invariant likelihood based testing of semi-linear models |
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Authors: | Jahar L Bhowmik Maxwell L King |
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Institution: | (1) Department of Econometrics and Business Statistics, Monash University, 3800 Clayton, Victoria, Australia |
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Abstract: | In this paper, we use a maximal invariant likelihood (MIL) to construct two likelihood ratio (LR) tests in the context of
a semi-linear regression model. The first involves testing for the inclusion of a non-linear regressor and the second involves
testing a linear regressor against the alternative of a non-linear regressor. We report the results of a Monte Carlo experiment
that compares the size and power properties of the traditional LR tests with those of our proposed MIL based LR tests. Our
simulation results show that in both cases, the MIL based tests have more accurate asymptotic critical values and better behaved
(i.e., better centred) power curves than their classical counterparts. |
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Keywords: | Likelihood Ratio Test Non-Linear Regression Monte Carlo Experiment Asymptotic Critical Value |
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