Maximal Invariant Likelihood Based Testing of Semi-Linear Models
AbstractIn this paper, we use a maximal invariant likelihood (MIL) to construct two likelihood ratio (LR) tests. The first involves testing for the inclusion of a non-linear regressor and the second involves testing of 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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Bibliographic InfoPaper provided by Econometric Society in its series Econometric Society 2004 Australasian Meetings with number 245.
Date of creation: 11 Aug 2004
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Likelihood ratio test; non-linear regressor; monte carlo experiment; asymptotic critical value;
Other versions of this item:
- Jahar Bhowmik & Maxwell King, 2007. "Maximal invariant likelihood based testing of semi-linear models," Statistical Papers, Springer, vol. 48(3), pages 357-383, September.
- 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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