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Relative Error Accurate Statistic Based on Nonparametric Likelihood

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  • Lorenzo Camponovo

    (University of Surrey)

  • Taisuke Otsu

    (London School of Economics)

Abstract

This paper develops a new test statistic for parameters defined by moment conditions that exhibits desirable relative error properties for the approximation of tail area probabilities. Our statistic, called the tilted exponential tilting (TET) statistic, is constructed by estimating certain cumulant generating function under exponential tilting weights. We show that the asymptotic p-value of the TET statistic can provide an accurate approximation to the p-value of an infeasible saddlepoint statistic, which is asymptotically chi-squared distributed with a relative error of order n^(-1) both in normal and large deviation regions. Numerical results illustrate the accuracy of the proposed TET statistic. Our results cover both just- and over-identified moment condition models.

Suggested Citation

  • Lorenzo Camponovo & Taisuke Otsu, 2018. "Relative Error Accurate Statistic Based on Nonparametric Likelihood," School of Economics Discussion Papers 0518, School of Economics, University of Surrey.
  • Handle: RePEc:sur:surrec:0518
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    File URL: https://repec.som.surrey.ac.uk/2018/DP05-18.pdf
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    1. Whitney K. Newey & Richard J. Smith, 2004. "Higher Order Properties of Gmm and Generalized Empirical Likelihood Estimators," Econometrica, Econometric Society, vol. 72(1), pages 219-255, January.
    2. Bryan W. Brown & Whitney K. Newey, 1998. "Efficient Semiparametric Estimation of Expectations," Econometrica, Econometric Society, vol. 66(2), pages 453-464, March.
    3. Yuichi Kitamura & Michael Stutzer, 1997. "An Information-Theoretic Alternative to Generalized Method of Moments Estimation," Econometrica, Econometric Society, vol. 65(4), pages 861-874, July.
    4. Veronika Czellar, 2010. "Accurate and Robust Tests for Indirect Inference," Post-Print hal-00553964, HAL.
    5. Guido W. Imbens & Richard H. Spady & Phillip Johnson, 1998. "Information Theoretic Approaches to Inference in Moment Condition Models," Econometrica, Econometric Society, vol. 66(2), pages 333-358, March.
    6. Veronika Czellar & Elvezio Ronchetti, 2010. "Accurate and Robust Tests for Indirect Inference," Post-Print hal-00585938, HAL.
    7. Ma, Yanyuan & Ronchetti, Elvezio, 2011. "Saddlepoint Test in Measurement Error Models," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 147-156.
    8. Veronika Czellar & Elvezio Ronchetti, 2010. "Accurate and Robust Tests for Indirect Inference," Post-Print hal-02313230, HAL.
    9. Veronika Czellar & Elvezio Ronchetti, 2010. "Accurate and robust tests for indirect inference," Biometrika, Biometrika Trust, vol. 97(3), pages 621-630.
    10. Jens Jensen & Andrew Wood, 1998. "Large Deviation and Other Results for Minimum Contrast Estimators," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 50(4), pages 673-695, December.
    11. Osipov, L. V., 1981. "On large deviations for sums of random vectors in Rk," Journal of Multivariate Analysis, Elsevier, vol. 11(2), pages 115-126, June.
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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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