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Non And Semi-Parametric Estimation In Models With Unknown Smoothness

Author

Listed:
  • Yulia Kotlyarova
  • Victoria Zinde-Walsh

Abstract

Many asymptotic results for kernel-based estimators were established under some smoothness assumption on density. For cases where smoothness assumptions that are used to derive unbiasedness or asymptotic rate may not hold we propose a combined estimator that could lead to the best available rate without knowledge of density smoothness. A Monte Carlo example confirms good performance of the combined estimator.

Suggested Citation

  • Yulia Kotlyarova & Victoria Zinde-Walsh, 2006. "Non And Semi-Parametric Estimation In Models With Unknown Smoothness," Departmental Working Papers 2006-15, McGill University, Department of Economics.
  • Handle: RePEc:mcl:mclwop:2006-15
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    File URL: http://www.mcgill.ca/files/economics/nonandsemiparametric.pdf
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    References listed on IDEAS

    as
    1. Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521355643, January.
    2. Zinde-Walsh, Victoria, 2002. "Asymptotic Theory For Some High Breakdown Point Estimators," Econometric Theory, Cambridge University Press, vol. 18(5), pages 1172-1196, October.
    3. Horowitz, Joel L, 1992. "A Smoothed Maximum Score Estimator for the Binary Response Model," Econometrica, Econometric Society, vol. 60(3), pages 505-531, May.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Marcia M Schafgans & Victoria Zinde-Walshyz, 2008. "Smoothness Adaptive AverageDerivative Estimation," STICERD - Econometrics Paper Series 529, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    2. Victoria Zinde-Walsh & Marcia M.A. Schafgans, 2007. "Robust Average Derivative Estimation," Departmental Working Papers 2007-12, McGill University, Department of Economics.
    3. Yulia Kotlyarova & Marcia M. A. Schafgans & Victoria Zinde-Walsh, 2021. "Rates of Expansions for Functional Estimators," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 121-139, December.
    4. Yulia Kotlyarova & Marcia M Schafgans & Victoria Zinde-Walsh, 2011. "Adapting Kernel Estimation to Uncertain Smoothness," STICERD - Econometrics Paper Series 557, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    5. Xiaohong Chen & David T. Jacho-Chávez & Oliver Linton, 2009. "An Alternative Way of ComputingEfficient Instrumental VariableEstimators," STICERD - Econometrics Paper Series 536, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    6. Victoria Zinde-Walsh, 2008. "Consequences of lack of smoothness in nonparametric estimation (in Russian)," Quantile, Quantile, issue 4, pages 57-69, March.
    7. Peter C. B. Phillips, 2017. "Reduced forms and weak instrumentation," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 818-839, October.
    8. Xiaohong Chen & David Jacho-Chávez & Oliver Linton, 2012. "Averaging of moment condition estimators," CeMMAP working papers 26/12, Institute for Fiscal Studies.
    9. Iglesias Emma M, 2010. "First and Second Order Asymptotic Bias Correction of Nonlinear Estimators in a Non-Parametric Setting and an Application to the Smoothed Maximum Score Estimator," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(3), pages 1-30, May.
    10. Antoine, Bertille & Renault, Eric, 2012. "Efficient minimum distance estimation with multiple rates of convergence," Journal of Econometrics, Elsevier, vol. 170(2), pages 350-367.
    11. D. F. Benoit & D. Van Den Poel, 2010. "Binary quantile regression: A Bayesian approach based on the asymmetric Laplace density," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 10/662, Ghent University, Faculty of Economics and Business Administration.
    12. Ali Habibnia & Esfandiar Maasoumi, 2021. "Forecasting in Big Data Environments: An Adaptable and Automated Shrinkage Estimation of Neural Networks (AAShNet)," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 363-381, December.
    13. repec:cep:stiecm:/2011/557 is not listed on IDEAS

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    JEL classification:

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

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