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Empirical likelihood specification testing in linear regression models

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  • Francesco Bravo

Abstract

This paper analyses the higher order asymptotic behaviour of a profiled empirical likelihood ratio which can be used as a specification test in linear regression models. Despite the presence of nuisance parameters, a simple Bartlett correction factor is obtained and used to improve to third order the accuracy of commonly used tests such as the inclusion of irrelevant variables without any distributional assumptions about the error process.

Suggested Citation

  • Francesco Bravo, "undated". "Empirical likelihood specification testing in linear regression models," Discussion Papers 00/28, Department of Economics, University of York.
    • Handle: RePEc:yor:yorken:00/28
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    File URL: https://www.york.ac.uk/media/economics/documents/discussionpapers/2000/0028.pdf
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    References listed on IDEAS

    as
    1. Chen, S. X., 1994. "Empirical Likelihood Confidence Intervals for Linear Regression Coefficients," Journal of Multivariate Analysis, Elsevier, vol. 49(1), pages 24-40, April.
    2. Song Chen, 1993. "On the accuracy of empirical likelihood confidence regions for linear regression model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 45(4), pages 621-637, December.
    3. Bravo, Francesco, 2004. "Empirical Likelihood Based Inference With Applications To Some Econometric Models," Econometric Theory, Cambridge University Press, vol. 20(2), pages 231-264, April.
    4. Francesco Bravo, "undated". "Bartlett-type Adjustments for Empirical Discrepancy Test Statistics," Discussion Papers 04/14, Department of Economics, University of York.
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    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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