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A Comparison of Marginal Likelihood Based and Approximate Point Optimal Tests for Random Regression Coefficient in the Presence of Autocorrelation

Author

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  • Rahman, S.
  • King, M.L.

Abstract

With respect to testing linear regression disturbances, two methods of test construction have recently been found to work well. These are traditional asymptotic tests based on the marginal likelihood or equivalently the likelihood of the maximal invariant and point optimal or approximate point optimal (APO) tests. The former approach has been found to work well for testing for random regression coefficients in the presence of autocorrelated errors. This paper constructs APO invariant (APOI) tests for this testing problem and extends the previous Monte Carlo study to include APOI tests. We conclude that for this testing problem, the extra work required to apply APOI tests hardly seems worthwhile, particularly for larger sample sizes.
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Suggested Citation

  • Rahman, S. & King, M.L., 1994. "A Comparison of Marginal Likelihood Based and Approximate Point Optimal Tests for Random Regression Coefficient in the Presence of Autocorrelation," Monash Econometrics and Business Statistics Working Papers 4/94, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:1994-4
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    Cited by:

    1. Sriananthakumar, Sivagowry & King, Maxwell L., 2006. "A new approximate point optimal test of a composite null hypothesis," Journal of Econometrics, Elsevier, vol. 130(1), pages 101-122, January.
    2. Jahar Bhowmik & Maxwell King, 2007. "Maximal invariant likelihood based testing of semi-linear models," Statistical Papers, Springer, vol. 48(3), pages 357-383, September.
    3. Sriananthakumar, Sivagowry, 2013. "Testing linear regression model with AR(1) errors against a first-order dynamic linear regression model with white noise errors: A point optimal testing approach," Economic Modelling, Elsevier, vol. 33(C), pages 126-136.
    4. Maxwell L. King & Sivagowry Sriananthakumar, 2015. "Point Optimal Testing: A Survey of the Post 1987 Literature," Monash Econometrics and Business Statistics Working Papers 5/15, Monash University, Department of Econometrics and Business Statistics.

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