Semiparametric Estimation of Nonseparable Models: A Minimum Distance from Independence Approach
AbstractThis paper focuses on nonseparable structural models of the form Y = m(X, U, Î±0) with U X and in which the structural parameter Î±0 contains both finite dimensional (Î¸0) and infinite dimensional (h0) unknown components. Our proposal is to estimate Î±0 by a minimum distance from independence (MDI) criterion. We show that: (i) our estimator of h0 is consistent and obtain rates of convergence; (ii) the estimator of Î¸0 is square root n consistent and asymptotically normally distributed.
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Bibliographic InfoPaper provided by Department of Economics, UC San Diego in its series University of California at San Diego, Economics Working Paper Series with number qt32k957bp.
Date of creation: 01 Mar 2009
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Nonparametric methods; Identification; estimation;
Other versions of this item:
- Ivana Komunjer & Andres Santos, 2010. "Semi-parametric estimation of non-separable models: a minimum distance from independence approach," Econometrics Journal, Royal Economic Society, vol. 13(3), pages S28-S55, October.
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