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Errors-in-Variables Models : A Generalized Functions Approach

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  • ZINDE-WALSH, Victoria

Abstract

Generalized functions are a powerful tool for examining errors-in-variables models, since they extend consideration to wide modelclasses. Schennach (Econometrica, 2007) - (S) applies this approach to prove identification in a general class of models. Here the problems addressed in (S) are revisited because various features of the generalized functions approach need to be clari?ed. The nonparametric identification theorem in (S) applies less generally than claimed (e.g. disallowing functions with fractional power growth) by relying on decomposition of generalized functions into ordinary and singular parts which may not hold. This paper highlights the issues of importance in applying generalized functions and provides the general nonparametric identification result relating it to possibility of estimation.

Suggested Citation

  • ZINDE-WALSH, Victoria, 2007. "Errors-in-Variables Models : A Generalized Functions Approach," Cahiers de recherche 14-2007, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  • Handle: RePEc:mtl:montec:14-2007
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    References listed on IDEAS

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    1. Zinde-Walsh, Victoria, 2008. "Kernel Estimation When Density May Not Exist," Econometric Theory, Cambridge University Press, vol. 24(03), pages 696-725, June.
    2. P. C. B. Phillips, 1985. "A Theorem on the Tail Behaviour of Probability Distributions with an Application to the Stable Family," Canadian Journal of Economics, Canadian Economics Association, vol. 18(1), pages 58-65, February.
    3. Victoria Zinde-Walsh & Peter C.B. Phillips, 2003. "Fractional Brownian Motion as a Differentiable Generalized Gaussian Process," Cowles Foundation Discussion Papers 1391, Cowles Foundation for Research in Economics, Yale University.
    4. Wang, Liqun & Hsiao, Cheng, 2011. "Method of moments estimation and identifiability of semiparametric nonlinear errors-in-variables models," Journal of Econometrics, Elsevier, vol. 165(1), pages 30-44.
    5. Hausman, Jerry A. & Newey, Whitney K. & Ichimura, Hidehiko & Powell, James L., 1991. "Identification and estimation of polynomial errors-in-variables models," Journal of Econometrics, Elsevier, vol. 50(3), pages 273-295, December.
    6. Wang, Liqun, 1998. "Estimation of censored linear errors-in-variables models," Journal of Econometrics, Elsevier, vol. 84(2), pages 383-400, June.
    7. Whitney K. Newey, 2001. "Flexible Simulated Moment Estimation Of Nonlinear Errors-In-Variables Models," The Review of Economics and Statistics, MIT Press, vol. 83(4), pages 616-627, November.
    8. Phillips, Peter C.B., 1995. "Robust Nonstationary Regression," Econometric Theory, Cambridge University Press, pages 912-951.
    9. Susanne M Schennach, 2007. "Instrumental Variable Estimation of Nonlinear Errors-in-Variables Models," Econometrica, Econometric Society, pages 201-239.
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    Citations

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    Cited by:

    1. Xavier d'Haultfoeuille, 2006. "On the Completeness Condition in Nonparametric Instrumental Problems," Working Papers 2006-32, Center for Research in Economics and Statistics.
    2. Xiaohong Chen & Han Hong & Denis Nekipelov, 2011. "Nonlinear Models of Measurement Errors," Journal of Economic Literature, American Economic Association, pages 901-937.
    3. D’Haultfoeuille, Xavier, 2011. "On The Completeness Condition In Nonparametric Instrumental Problems," Econometric Theory, Cambridge University Press, vol. 27(03), pages 460-471, June.

    More about this item

    Keywords

    errors-in-variables model; generalized functions;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools

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