Iterative estimation of solutions to noisy nonlinear operator equations in nonparametric instrumental regression
2014 | journal article. A publication with affiliation to the University of Göttingen.
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Iterative estimation of solutions to noisy nonlinear operator equations in nonparametric instrumental regression
Dunker, F.; Florens, J.-P.; Hohage, T. ; Johannes, J. & Mammen, E. (2014)
Journal of Econometrics, 178 pp. 444-455. DOI: https://doi.org/10.1016/j.jeconom.2013.06.001
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Details
- Authors
- Dunker, Fabian; Florens, Jean-Pierre; Hohage, Thorsten ; Johannes, Jan; Mammen, Enno
- Abstract
- This paper discusses the solution of nonlinear integral equations with noisy integral kernels as they appear in nonparametric instrumental regression. We propose a regularized Newton-type iteration and establish convergence and convergence rate results. A particular emphasis is on instrumental regression models where the usual conditional mean assumption is replaced by a stronger independence assumption. We demonstrate for the case of a binary instrument that our approach allows the correct estimation of regression functions which are not identifiable with the standard model. This is illustrated in computed examples with simulated data.
- Issue Date
- 2014
- Journal
- Journal of Econometrics
- Organization
- Institut für Numerische und Angewandte Mathematik
- Working Group
- RG Hohage (Inverse Problems)
- Language
- English