Errors-in-Variables Models : A Generalized Functions Approach
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.
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- Zinde-Walsh, Victoria, 2008. "Kernel Estimation When Density May Not Exist," Econometric Theory, Cambridge University Press, vol. 24(03), pages 696-725, June.
- 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.
- 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.
- 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.
- 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.
- Wang, Liqun, 1998. "Estimation of censored linear errors-in-variables models," Journal of Econometrics, Elsevier, vol. 84(2), pages 383-400, June.
- 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.
- 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.
- Phillips, Peter C.B., 1995. "Robust Nonstationary Regression," Econometric Theory, Cambridge University Press, vol. 11(05), pages 912-951, October.
- Peter C.B. Phillips, 1993. "Robust Nonstationary Regression," Cowles Foundation Discussion Papers 1064, Cowles Foundation for Research in Economics, Yale University.
- Susanne M Schennach, 2007. "Instrumental Variable Estimation of Nonlinear Errors-in-Variables Models," Econometrica, Econometric Society, vol. 75(1), pages 201-239, 01.
- Susanne M. Schennach, 2004. "Instrumental Variable Estimation of Nonlinear Errors-in-Variables Models," Econometric Society 2004 North American Summer Meetings 602, Econometric Society.