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Testing for Non-Normality in the Presence of One-Sided Slope Parameters

Author

Listed:
  • Anthony W. Hughes

    (School of Economics, University of Adelaide)

Abstract

In a recent paper, Hughes (1999) showed that the power of tests of linear regression parameters could be improved by utilizing one-sided information regarding the nuisance parameters in the testing problem. In this paper, we extend this principle to the problem of diagnosing departures from the assumption of normality in linear regression residuals. We show that the asymptotic theory of the popular normality test developed by Jarque and Bera (1987) is also applicable when inequality constraints are imposed on the slope parameters. Monte Carlo evidence is then presented which suggests that the size of tests based on inequality constrained residuals is roughly equivalent to the size of tests based on unconstrained residuals using both asymptotic and bootstrap critical values. We then demonstrate that significant improvements in the power of the Jarque-Bera test can be made via the application of one-sided information concerning the slope parameters in the model.

Suggested Citation

  • Anthony W. Hughes, 2000. "Testing for Non-Normality in the Presence of One-Sided Slope Parameters," School of Economics and Public Policy Working Papers 2000-01, University of Adelaide, School of Economics and Public Policy.
    • Handle: RePEc:adl:wpaper:2000-01
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    File URL: https://media.adelaide.edu.au/economics/papers/doc/wp2000-01.pdf
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    References listed on IDEAS

    as
    1. Gourieroux, Christian & Holly, Alberto & Monfort, Alain, 1982. "Likelihood Ratio Test, Wald Test, and Kuhn-Tucker Test in Linear Models with Inequality Constraints on the Regression Parameters," Econometrica, Econometric Society, vol. 50(1), pages 63-80, January.
    2. Gourieroux, Christian & Holly, Alberto & Monfort, Alain, 1981. "Kuhn-Tucker, likelihood ratio and Wald tests for nonlinear models with inequality constraints on the parameters," Journal of Econometrics, Elsevier, vol. 16(1), pages 166-166, May.
    Full references (including those not matched with items on IDEAS)

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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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