SSRN Author: Xiaohong ChenXiaohong Chen SSRN Content
http://www.ssrn.com/author=30333
http://www.ssrn.com/rss/en-usSat, 30 Sep 2017 01:34:55 GMTeditor@ssrn.com (Editor)Sat, 30 Sep 2017 01:34:55 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0New: Monte Carlo Confidence Sets for Identified SetsIn complicated/nonlinear parametric models, it is generally hard to know whether the model parameters are point identified. We provide computationally attractive procedures to construct confidence sets (CSs) for identified sets of full parameters and of subvectors in models defined through a likelihood or a vector of moment equalities or inequalities. These CSs are based on level sets of optimal sample criterion functions (such as likelihood or optimally-weighted or continuously-updated GMM criterions). The level sets are constructed using cutoffs that are computed via Monte Carlo (MC) simulations directly from the quasi-posterior distributions of the criterions. We establish new Bernstein-von Mises (or Bayesian Wilks) type theorems for the quasi-posterior distributions of the quasi-likelihood ratio (QLR) and profile QLR in partially-identified regular models and some non-regular models. These results imply that our MC CSs have exact asymptotic frequentist coverage for identified ...
http://www.ssrn.com/abstract=3043470
http://www.ssrn.com/1629366.htmlFri, 29 Sep 2017 06:59:53 GMTREVISION: Optimal Sup-Norm Rates and Uniform Inference on Nonlinear Functionals of Nonparametric IV RegressionThis paper makes several important contributions to the literature about nonparametric instrumental variables (NPIV) estimation and inference on a structural function h<sub>0</sub> and its functionals. First, we derive sup-norm convergence rates for computationally simple sieve NPIV (series 2SLS) estimators of h<sub>0</sub> and its derivatives. Second, we derive a lower bound that describes the best possible (minimax) sup-norm rates of estimating h<sub>0</sub> and its derivatives, and show that the sieve NPIV estimator can attain the minimax rates when h<sub>0</sub> is approximated via a spline or wavelet sieve. Our optimal sup-norm rates surprisingly coincide with the optimal root-mean-squared rates for severely ill-posed problems, and are only a logarithmic factor slower than the optimal root-mean-squared rates for mildly ill-posed problems. Third, we use our sup-norm rates to establish the uniform Gaussian process strong approximations and the score bootstrap uniform confidence ...
http://www.ssrn.com/abstract=2916740
http://www.ssrn.com/1586828.htmlSat, 29 Apr 2017 08:50:32 GMTREVISION: Optimal Sup-Norm Rates and Uniform Inference on Nonlinear Functionals of Nonparametric IV RegressionThis paper makes several important contributions to the literature about nonparametric instrumental variables (NPIV) estimation and inference on a structural function h<sub>0</sub> and its functionals. First, we derive sup-norm convergence rates for computationally simple sieve NPIV (series 2SLS) estimators of h<sub>0</sub> and its derivatives. Second, we derive a lower bound that describes the best possible (minimax) sup-norm rates of estimating h<sub>0</sub> and its derivatives, and show that the sieve NPIV estimator can attain the minimax rates when h<sub>0</sub> is approximated via a spline or wavelet sieve. Our optimal sup-norm rates surprisingly coincide with the optimal root-mean-squared rates for severely ill-posed problems, and are only a logarithmic factor slower than the optimal root-mean-squared rates for mildly ill-posed problems. Third, we use our sup-norm rates to establish the uniform Gaussian process strong approximations and the score bootstrap uniform confidence ...
http://www.ssrn.com/abstract=2916740
http://www.ssrn.com/1566648.htmlWed, 15 Feb 2017 06:01:07 GMT