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Examples of L^2-Complete and Boundedly-Complete Distributions

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Abstract

Completeness and bounded-completeness conditions are used increasingly in econometrics to obtain nonparametric identification in a variety of models from nonparametric instrumental variable regression to non-classical measurement error models. However, distributions that are known to be complete or boundedly complete are somewhat scarce. In this paper, we consider an L^2-completeness condition that lies between completeness and bounded completeness. We construct broad (nonparametric) classes of distributions that are L^2-complete and boundedly complete. The distributions can have any marginal distributions and a wide range of strengths of dependence. Examples of L^2-incomplete distributions also are provided.

Suggested Citation

  • Donald W.K. Andrews, 2011. "Examples of L^2-Complete and Boundedly-Complete Distributions," Cowles Foundation Discussion Papers 1801, Cowles Foundation for Research in Economics, Yale University.
  • Handle: RePEc:cwl:cwldpp:1801
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    File URL: http://cowles.yale.edu/sites/default/files/files/pub/d18/d1801.pdf
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    References listed on IDEAS

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    1. D’Haultfoeuille, Xavier, 2011. "On The Completeness Condition In Nonparametric Instrumental Problems," Econometric Theory, Cambridge University Press, vol. 27(03), pages 460-471, June.
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    Cited by:

    1. Xiaohong Chen & Andres Santos, 2015. "Overidentification in Regular Models," Cowles Foundation Discussion Papers 1999, Cowles Foundation for Research in Economics, Yale University.
    2. Fève, Frédérique & Florens, Jean-Pierre, 2014. "Non parametric analysis of panel data models with endogenous variables," Journal of Econometrics, Elsevier, vol. 181(2), pages 151-164.
    3. Mavroeidis, Sophocles & Sasaki, Yuya & Welch, Ivo, 2015. "Estimation of heterogeneous autoregressive parameters with short panel data," Journal of Econometrics, Elsevier, vol. 188(1), pages 219-235.
    4. Manuel Arellano & Stéphane Bonhomme, 2015. "Nonlinear panel data estimation via quantile regressions," CeMMAP working papers CWP40/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Manuel Arellano & Richard Blundell & Stéphane Bonhomme, 2017. "Earnings and Consumption Dynamics: A Nonlinear Panel Data Framework," Econometrica, Econometric Society, vol. 85, pages 693-734, May.
    6. Song, Suyong, 2015. "Semiparametric estimation of models with conditional moment restrictions in the presence of nonclassical measurement errors," Journal of Econometrics, Elsevier, vol. 185(1), pages 95-109.
    7. Fabian Dunker, 2015. "Convergence of the risk for nonparametric IV quantile regression and nonparametric IV regression with full independence," Papers 1511.03977, arXiv.org.
    8. Kim, Kyoo il & Petrin, Amil & Song, Suyong, 2016. "Estimating production functions with control functions when capital is measured with error," Journal of Econometrics, Elsevier, vol. 190(2), pages 267-279.
    9. Hidehiko Ichimura & Whitney K. Newey, 2017. "The influence function of semiparametric estimators," CeMMAP working papers CWP06/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. repec:eee:econom:v:200:y:2017:i:1:p:48-58 is not listed on IDEAS

    More about this item

    Keywords

    Bivariate distribution; Bounded completeness; Canonical correlation; Completeness; Identification; Measurement error; Nonparametric instrumental variable regression;

    JEL classification:

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

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