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Robust Data-Driven Inference for Density-Weighted Average Derivatives

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Author Info

  • Matias D. Cattaneo

    (Department of Economics, University of Michigan)

  • Richard K. Crump

    (Federal Reserve Bank of New York)

  • Michael Jansson

    ()
    (Department of Economics, UC Berkeley and CREATES)

Abstract

This paper presents a new data-driven bandwidth selector compatible with the small bandwidth asymptotics developed in Cattaneo, Crump, and Jansson (2009) for density- weighted average derivatives. The new bandwidth selector is of the plug-in variety, and is obtained based on a mean squared error expansion of the estimator of interest. An extensive Monte Carlo experiment shows a remarkable improvement in performance when the bandwidth- dependent robust inference procedure proposed by Cattaneo, Crump, and Jansson (2009) is coupled with this new data-driven bandwidth selector. The resulting robust data-driven confi- dence intervals compare favorably to the alternative procedures available in the literature.

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Bibliographic Info

Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2009-46.

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Length: 36
Date of creation: 28 Sep 2009
Date of revision:
Handle: RePEc:aah:create:2009-46

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Web page: http://www.econ.au.dk/afn/

Related research

Keywords: Average derivatives; Bandwidth selection; Robust inference; Small bandwidth asymptotics;

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References

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  1. Matias D. Cattaneo & Richard K. Crump & Michael Jansson, 2008. "Small Bandwidth Asymptotics for Density-Weighted Average Derivatives," CREATES Research Papers 2008-24, School of Economics and Management, University of Aarhus.
  2. Haerdle,W. & Hart,J.D. & Marron,J.S. & Tsybakov,A.B., 1989. "Bandwidth choice for average derivative estimation," Discussion Paper Serie A 200, University of Bonn, Germany.
  3. Hardle, W. & Tsybakov, A.B., 1992. "How Sensitive are Average Derivatives?," Papers 9208, Tilburg - Center for Economic Research.
  4. Y. Nishiyama & P. M. Robinson, 2000. "Edgeworth Expansions for Semiparametric Averaged Derivatives," Econometrica, Econometric Society, vol. 68(4), pages 931-980, July.
  5. Horowitz, Joel & Hardle, Wolfgang, 1994. "Direct Semiparametric Estimation of Single-Index Models With Discrete Covariates," Working Papers 94-22, University of Iowa, Department of Economics.
  6. Hidehiko Ichimura & Petra E. Todd, 2006. "Implementing Nonparametric and Semiparametric Estimators," CIRJE F-Series CIRJE-F-452, CIRJE, Faculty of Economics, University of Tokyo.
  7. Robinson, P M, 1995. "The Normal Approximation for Semiparametric Averaged Derivatives," Econometrica, Econometric Society, vol. 63(3), pages 667-80, May.
  8. Newey, W.K., 1989. "The Asymptotic Variance Of Semiparametric Estimotors," Papers 346, Princeton, Department of Economics - Econometric Research Program.
  9. Haerdle,Wolfgang & Stoker,Thomas, 1987. "Investigations smooth multiple regression by the method of average derivatives," Discussion Paper Serie A 107, University of Bonn, Germany.
  10. Whitney K. Newey & Fushing Hsieh & James M. Robins, 2004. "Twicing Kernels and a Small Bias Property of Semiparametric Estimators," Econometrica, Econometric Society, vol. 72(3), pages 947-962, 05.
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Citations

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Cited by:
  1. Matias D. Cattaneo & Michael Jansson & Whitney K. Newey, 2012. "Alternative Asymptotics and the Partially Linear Model with Many Regressors," CREATES Research Papers 2012-02, School of Economics and Management, University of Aarhus.
  2. Donald W.K. Andrews & Xiaoxia Shi, 2011. "Nonparametric Inference Based on Conditional Moment Inequalities," Cowles Foundation Discussion Papers 1840, Cowles Foundation for Research in Economics, Yale University.
  3. Hiroaki Kaido, 2014. "Asymptotically efficient estimation of weighted average derivatives with an interval censored variable," CeMMAP working papers CWP03/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  4. Atchadé, Yves F. & Cattaneo, Matias D., 2014. "A martingale decomposition for quadratic forms of Markov chains (with applications)," Stochastic Processes and their Applications, Elsevier, vol. 124(1), pages 646-677.

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