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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
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    File URL: https://repec.econ.au.dk/repec/creates/rp/10/rp10_23.pdf
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    Cited by:

    1. 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.
    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. Matias D Cattaneo & Michael Jansson & Xinwei Ma, 2019. "Two-Step Estimation and Inference with Possibly Many Included Covariates," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 86(3), pages 1095-1122.
    4. Paulo M. D. C. Parente & Richard J. Smith, 2021. "Quasi‐maximum likelihood and the kernel block bootstrap for nonlinear dynamic models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(4), pages 377-405, July.
    5. Clara Lia Machado & Carlos León & Miguel Sarmiento & Orlando Chipatecua, 2010. "Riesgo Sistémico y Estabilidad del Sistema de Pagos de Alto Valor en Colombia: Análisis bajo Topología de Redes y Simulación de Pagos," Borradores de Economia 7669, Banco de la Republica.
    6. Paulo M.D.C. Parente & Richard J. Smith, 2018. "Generalised Empirical Likelihood Kernel Block Bootstrapping," Working Papers REM 2018/55, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    7. 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," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 29(65), pages 106-175, June.
    8. Konrad Menzel, 2021. "Bootstrap With Cluster‐Dependence in Two or More Dimensions," Econometrica, Econometric Society, vol. 89(5), pages 2143-2188, September.
    9. repec:cep:stiecm:/2011/557 is not listed on IDEAS
    10. Robert Griffiths & Richard Barron & Michelle Gleeson & Mark Danese & Anthony O’Hagan & Victoria Chia & Jason Legg & Gary Lyman, 2012. "Granulocyte-Colony Stimulating Factor Use and Medical Costs after Initial Adjuvant Chemotherapy in Older Patients with Early-Stage Breast Cancer," PharmacoEconomics, Springer, vol. 30(2), pages 103-118, February.

    More about this item

    Keywords

    Averaged derivatives; Bootstrap; Small bandwidth asymptotics;
    All these keywords.

    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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