SSRN Author: Xiaohong ChenXiaohong Chen SSRN Content
http://www.ssrn.com/author=30333
http://www.ssrn.com/rss/en-usSun, 06 Apr 2014 02:33:49 GMTeditor@ssrn.com (Editor)Sun, 06 Apr 2014 02:33:49 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0New: Sieve Wald and QLR Inferences on Semi/Nonparametric Conditional Moment ModelsThis paper considers inference on functionals of semi/nonparametric conditional moment restrictions with possibly nonsmooth generalized residuals, which include all of the (nonlinear) nonparametric instrumental variables (IV) as special cases. For these models it is often difficult to verify whether a functional is regular (i.e., root-n estimable) or irregular (i.e., slower than root-n estimable). We provide computationally simple, unified inference procedures that are asymptotically valid regardless of whether a functional is regular or not. We establish the following new useful results: (1) the asymptotic normality of a plug-in penalized sieve minimum distance (PSMD) estimator of a (possibly irregular) functional; (2) the consistency of simple sieve variance estimators of the plug-in PSMD estimator, and hence the asymptotic chi-square distribution of the sieve Wald statistic; (3) the asymptotic chi-square distribution of an optimally weighted sieve quasi likelihood ratio (QLR) test ...
http://www.ssrn.com/abstract=2420116
http://www.ssrn.com/1295711.htmlSat, 05 Apr 2014 10:35:11 GMTNew: Optimal Uniform Convergence Rates for Sieve Nonparametric Instrumental Variables RegressionWe study the problem of nonparametric regression when the regressor is endogenous, which is an important nonparametric instrumental variables (NPIV) regression in econometrics and a difficult ill-posed inverse problem with unknown operator in statistics. We first establish a general upper bound on the sup-norm (uniform) convergence rate of a sieve estimator, allowing for endogenous regressors and weakly dependent data. This result leads to the optimal sup-norm convergence rates for spline and wavelet least squares regression estimators under weakly dependent data and heavy-tailed error terms. This upper bound also yields the sup-norm convergence rates for sieve NPIV estimators under i.i.d. data: the rates coincide with the known optimal L^2-norm rates for severely ill-posed problems, and are power of log(n) slower than the optimal L^2-norm rates for mildly ill-posed problems. We then establish the minimax risk lower bound in sup-norm loss, which coincides with our upper bounds on ...
http://www.ssrn.com/abstract=2349684
http://www.ssrn.com/1255245.htmlMon, 04 Nov 2013 07:45:44 GMTNew: Sieve Quasi Likelihood Ratio Inference on Semi/Nonparametric Conditional Moment ModelsThis paper considers inference on functionals of semi/nonparametric conditional moment restrictions with possibly nonsmooth generalized residuals. These models belong to the difficult (nonlinear) ill-posed inverse problems with unknown operators, and include all of the (nonlinear) nonparametric instrumental variables (IV) as special cases. For these models it is generally difficult to verify whether a functional is regular (i.e., root-n estimable) or irregular (i.e., slower than root-n estimable
http://www.ssrn.com/abstract=2271617
http://www.ssrn.com/1209966.htmlThu, 30 May 2013 21:59:02 GMTNew: Likelihood Inference in Some Finite Mixture ModelsParametric mixture models are commonly used in applied work, especially empirical economics, where these models are often employed to learn for example about the proportions of various types in a given population. This paper examines the inference question on the proportions (mixing probability) in a simple mixture model in the presence of nuisance parameters when sample size is large. It is well known that likelihood inference in mixture models is complicated due to 1) lack of point identificat
http://www.ssrn.com/abstract=2264368
http://www.ssrn.com/1205012.htmlTue, 14 May 2013 16:15:39 GMT