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Errors-In-Variables Models: A Generalized Functions Approach

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  • Victoria Zinde-Walsh

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Abstract

Identification in errors-in-variables regression models was recently extended to wide models classes by S. Schennach (Econometrica, 2007) (S) via use of generalized functions. In this paper the problems of non- and semi- parametric identification in such models are re-examined. Nonparametric identification holds under weaker assumptions than in (S); the proof here does not rely on decomposition of generalized functions into ordinary and singular parts, which may not hold. Conditions for continuity of the identification mapping are provided and a consistent nonparametric plug-in estimator for regression functions in the L₁ space constructed. Semiparametric identification via a finite set of moments is shown to hold for classes of functions that are explicitly characterized; unlike (S) existence of a moment generating function for the measurement

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Bibliographic Info

Paper provided by McGill University, Department of Economics in its series Departmental Working Papers with number 2009-09.

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Length: 37 pages
Date of creation: Sep 2009
Date of revision:
Handle: RePEc:mcl:mclwop:2009-09

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  1. Wang, Liqun, 1998. "Estimation of censored linear errors-in-variables models," Journal of Econometrics, Elsevier, vol. 84(2), pages 383-400, June.
  2. 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.
  3. Susanne M. Schennach, 2004. "Instrumental Variable Estimation of Nonlinear Errors-in-Variables Models," Econometric Society 2004 North American Summer Meetings 602, Econometric Society.
  4. Phillips, Peter C.B., 1995. "Robust Nonstationary Regression," Econometric Theory, Cambridge University Press, vol. 11(05), pages 912-951, October.
  5. Whitney Newey, 1999. "Flexible Simulated Moment Estimation of Nonlinear Errors-in-Variables Models," Working papers 99-02, Massachusetts Institute of Technology (MIT), Department of Economics.
  6. 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.
  7. 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.
  8. Zinde-Walsh, Victoria, 2008. "Kernel Estimation When Density May Not Exist," Econometric Theory, Cambridge University Press, vol. 24(03), pages 696-725, June.
  9. 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.
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Cited by:
  1. Xiaohong Chen & Han Hong & Denis Nekipelov, 2011. "Nonlinear Models of Measurement Errors," Journal of Economic Literature, American Economic Association, vol. 49(4), pages 901-37, December.
  2. 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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