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Maximal Invariant Likelihood Based Testing of Semi-Linear Models

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  • Maxwell L. King
  • Jahar L. Bhowmik

Abstract

In 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

Suggested Citation

  • Maxwell L. King & Jahar L. Bhowmik, 2004. "Maximal Invariant Likelihood Based Testing of Semi-Linear Models," Econometric Society 2004 Australasian Meetings 245, Econometric Society.
    • Handle: RePEc:ecm:ausm04:245
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    References listed on IDEAS

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    1. McManus, Douglas A. & Nankervis, John C. & Savin, N. E., 1994. "Multiple optima and asymptotic approximations in the partial adjustment model," Journal of Econometrics, Elsevier, vol. 62(2), pages 91-128, June.
    2. Martin, Vance L., 1998. "Econometric Society Australasian Meetings 1997 (ESAM97)," Econometric Theory, Cambridge University Press, vol. 14(06), pages 800-801, December.
    3. Laskar, Mizan R. & King, Maxwell L., 1997. "Modified Wald test for regression disturbances," Economics Letters, Elsevier, vol. 56(1), pages 5-11, September.
    4. Ara, Ismat & King, Maxwell L., "undated". "Marginal Likelihood Based Tests of a Subvector of the Parameter Vector of Linear Regression Disturbances," Department of Econometrics and Business Statistics Working Papers 267765, Monash University, Department of Econometrics and Business Statistics.
    5. Rahman, Shahidur & King, Maxwell L., "undated". "A Comparison of Marginal Likelihood Based and Approximate Point Optimal Tests for Random Regression Coefficients in the Presence of Autocorrelation," Department of Econometrics and Business Statistics Working Papers 267434, Monash University, Department of Econometrics and Business Statistics.
    6. Rahman, Shahidur & King, Maxwell L., 1997. "Marginal-likelihood score-based tests of regression disturbances in the presence of nuisance parameters," Journal of Econometrics, Elsevier, vol. 82(1), pages 81-106.
    7. Laskar, Mizan R. & King, Maxwell L., "undated". "Modified Likelihood and Related Methods for Handling Nuisance Parameters in the Linear Regression Model," Department of Econometrics and Business Statistics Working Papers 267941, Monash University, Department of Econometrics and Business Statistics.
    8. Konstas, Panos & Khouja, Mohamad W, 1969. "The Keynesian Demand-for-Money Function: Another Look and Some Additional Evidence," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 1(4), pages 765-777, November.
    9. Moulton, Brent R & Randolph, William C, 1989. "Alternative Tests of the Error Components Model," Econometrica, Econometric Society, vol. 57(3), pages 685-693, May.
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    2. Grünwald, Peter & Koolen, Wouter M., 2026. "Supermartingales for one-sided tests: Sufficient monotone likelihood ratios are sufficient," Statistics & Probability Letters, Elsevier, vol. 229(C).

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    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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