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Bootstrapping Density-Weighted Average Derivatives

Author

Listed:
  • Matias D. Cattaneo

    () (University of Michigan)

  • Richard K. Crump

    () (Federal Reserve Bank of New York)

  • Michael Jansson

    (UC Berkeley and CREATES)

Abstract

Employing the "small bandwidth" asymptotic framework of Cattaneo, Crump, and Jansson (2009), this paper studies the properties of a variety of bootstrap-based inference procedures associated with the kernel-based density-weighted averaged derivative estimator proposed by Powell, Stock, and Stoker (1989). In many cases validity of bootstrap-based inference procedures is found to depend crucially on whether the bandwidth sequence satisfies a particular (asymptotic linearity) condition. An exception to this rule occurs for inference procedures involving a studentized estimator employing a "robust" variance estimator derived from the "small bandwidth" asymptotic framework. The results of a small-scale Monte Carlo experiment are found to be consistent with the theory and indicate in particular that sensitivity with respect to the bandwidth choice can be ameliorated by using the "robust"variance estimatorClassification-JEL: C12, C14, C21, C24

Suggested Citation

  • Matias D. Cattaneo & Richard K. Crump & Michael Jansson, 2010. "Bootstrapping Density-Weighted Average Derivatives," CREATES Research Papers 2010-23, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:create:2010-23
    as

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    File URL: ftp://ftp.econ.au.dk/creates/rp/10/rp10_23.pdf
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    References listed on IDEAS

    as
    1. León, José & Ludeña, Carenne, 2007. "Limits for weighted p-variations and likewise functionals of fractional diffusions with drift," Stochastic Processes and their Applications, Elsevier, vol. 117(3), pages 271-296, March.
    2. Breuer, Péter & Major, Péter, 1983. "Central limit theorems for non-linear functionals of Gaussian fields," Journal of Multivariate Analysis, Elsevier, vol. 13(3), pages 425-441, September.
    3. Barndorff-Nielsen, Ole E. & Corcuera, José Manuel & Podolskij, Mark, 2009. "Power variation for Gaussian processes with stationary increments," Stochastic Processes and their Applications, Elsevier, vol. 119(6), pages 1845-1865, June.
    4. Corinne Berzin & José León, 2007. "Estimating the Hurst Parameter," Statistical Inference for Stochastic Processes, Springer, vol. 10(1), pages 49-73, January.
    5. Ole E. Barndorff-Nielsen & José Manuel Corcuera & Mark Podolskij, 2009. "Multipower Variation for Brownian Semistationary Processes," CREATES Research Papers 2009-21, Department of Economics and Business Economics, Aarhus University.
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    Cited by:

    1. Clara Machado & Carlos León & Miguel Sarmiento & Freddy Cepeda & Orlando Chipatecua & Jorge Cely, 2011. "Riesgo Sistémico Y Estabilidad Del Sistema De Pagos De Alto Valor En Colombia: Análisis Bajo," Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 29(65), pages 106-175, Junio.
    2. 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.
    3. repec:cep:stiecm:/2011/557 is not listed on IDEAS

    More about this item

    Keywords

    Averaged derivatives; Bootstrap; Small bandwidth asymptotics;

    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
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models

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