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Chen, Xiaohong
Pouzo, Demian
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cemmap working paper CWP20/09
This paper considers semiparametric efficient estimation of conditional moment models with possibly nonsmooth residuals in unknown parametric components (θ) and unknown functions (h) of endogenous variables. We show that: (1) the penalized sieve minimum distance(PSMD) estimator (ˆθ,ˆh) can simultaneously achieve root-n asymptotic normality of ˆθ and nonparametric optimal convergence rate of h, allowing for noncompact function parameter spaces; (2) a simple weighted bootstrap procedure consistently estimates the limiting distribution of the PSMD ˆθ ; (3) the semiparametric efficiency bound formula of Ai and Chen (2003) remains valid for conditional models with nonsmooth residuals, and the optimally weighted PSMD estimator achieves the bound; (4) the centered, profiled optimally weighted PSMD criterion is asymptotically chi-square distributed. We illustrate our theories using a partially linear quantile instrumental variables (IV) regression, a Monte Carlo study, and an empirical estimation of the shape-invariant quantile IV Engel curves.
Penalized sieve minimum distance
Nonsmooth generalized residuals
Nonlinear nonparametric endogeneity
Weighted bootstrap
Semiparametric efficiency
Confidence region
Partially linear quantile IV regression
Shape-invariant quantile IV Engel curves
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Working Paper

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