Estimation of Nonparametric Conditional Moment Models with Possibly Nonsmooth Generalized Residuals
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This paper studies nonparametric estimation of conditional moment models in which the generalized residual functions can be nonsmooth in the unknown functions of endogenous variables. This is a nonparametric nonlinear instrumental variables (IV) problem. We propose a class of penalized sieve minimum distance (PSMD) estimators which are minimizers of a penalized empirical minimum distance criterion over a collection of sieve spaces that are dense in the inﬁnite dimensional function parameter space. Some of the PSMD procedures use slowly growing ﬁnite dimensional sieves with flexible penalties or without any penalty; some use large dimensional sieves with lower semicompact and/or convex penalties. We establish their consistency and the convergence rates in Banach space norms (such as a sup-norm or a root mean squared norm), allowing for possibly non-compact inﬁnite dimensional parameter spaces. For both mildly and severely ill-posed nonlinear inverse problems, our convergence rates in Hilbert space norms (such as a root mean squared norm) achieve the known minimax optimal rate for the nonparametric mean IV regression. We illustrate the theory with a nonparametric additive quantile IV regression. We present a simulation study and an empirical application of estimating nonparametric quantile IV Engel curves.
Chen, Xiaohong and Pouzo, Demian, "Estimation of Nonparametric Conditional Moment Models with Possibly Nonsmooth Generalized Residuals" (2008). Cowles Foundation Discussion Papers. 1952.