SSRN Author: Xiaohong ChenXiaohong Chen SSRN Content
http://www.ssrn.com/author=30333
http://www.ssrn.com/rss/en-usFri, 08 Jul 2016 01:26:07 GMTeditor@ssrn.com (Editor)Fri, 08 Jul 2016 01:26:07 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0New: MCMC Condence Sets for Identied SetsIn complicated/nonlinear parametric models, it is generally hard to determine whether the model parameters are (globally) point identified. We provide computationally attractive procedures to construct confidence sets (CSs) for identified sets of parameters in econometric models defined through a likelihood or a vector of moments. The CSs for the identified set or for a function of the identified set (such as a subvector) are based on inverting an optimal sample criterion (such as likelihood or continuously updated GMM), where the cutoff values are computed via Monte Carlo simulations directly from a quasi posterior distribution of the criterion. We establish new Bernstein-von Mises type theorems for the posterior distributions of the quasi-likelihood ratio (QLR) and profile QLR statistics in partially identified models, allowing for singularities. These results imply that the Monte Carlo criterion-based CSs have correct frequentist coverage for the identified set as the sample size ...
http://www.ssrn.com/abstract=2805597
http://www.ssrn.com/1510698.htmlThu, 07 Jul 2016 20:59:08 GMTNew: MCMC Confidence Sets for Identified SetsIn complicated/nonlinear parametric models, it is hard to determine whether a parameter of interest is formally point identified. We provide computationally attractive procedures to construct confidence sets (CSs) for identified sets of parameters in econometric models defined through a likelihood or a vector of moments. The CSs for the identified set or for a function of the identified set (such as a subvector) are based on inverting an optimal sample criterion (such as likelihood or continuously updated GMM), where the cutoff values are computed directly from Markov Chain Monte Carlo (MCMC) simulations of a quasi posterior distribution of the criterion. We establish new Bernstein-von Mises type theorems for the posterior distributions of the quasi-likelihood ratio (QLR) and profile QLR statistics in partially identified models, allowing for singularities. These results imply that the MCMC criterion-based CSs have correct frequentist coverage for the identified set as the sample ...
http://www.ssrn.com/abstract=2775253
http://www.ssrn.com/1493998.htmlThu, 05 May 2016 12:36:35 GMTREVISION: Simple Nonparametric Estimators for the Bid-Ask Spread in the Roll ModelWe propose new methods for estimating the bid-ask spread from observed transaction prices alone. Our methods are based on the empirical characteristic function instead of the sample autocovariance function like the method of Roll (1984). As in Roll (1984), we have a closed form expression for the spread, but this is only based on a limited amount of the model-implied identification restrictions. We also provide methods that take account of more identification information. We compare our methods theoretically and numerically with the Roll method as well as with its best known competitor, the Hasbrouck (2004) method, which uses a Bayesian Gibbs methodology under a Gaussian assumption. Our estimators are competitive with Roll's and Hasbrouck's when the latent true fundamental return distribution is Gaussian, and perform much better when this distribution is far from Gaussian. Our methods are applied to the E-mini futures contract on the S&P 500 during the Flash Crash of May 6, 2010. ...
http://www.ssrn.com/abstract=2748858
http://www.ssrn.com/1488683.htmlMon, 18 Apr 2016 14:57:54 GMTNew: Methods for Nonparametric and Semiparametric Regressions with Endogeneity: A Gentle GuideThis paper reviews recent advances in estimation and inference for nonparametric and semiparametric models with endogeneity. It first describes methods of sieves and penalization for estimating unknown functions identified via conditional moment restrictions. Examples include nonparametric instrumental variables regression (NPIV), nonparametric quantile IV regression and many more semi-nonparametric structural models. Asymptotic properties of the sieve estimators and the sieve Wald, quasi-likelihood ratio (QLR) hypothesis tests of functionals with nonparametric endogeneity are presented. For sieve NPIV estimation, the rate-adaptive data-driven choices of sieve regularization parameters and the sieve score bootstrap uniform confidence bands are described. Finally, simple sieve variance estimation and over-identification test for semiparametric two-step GMM are reviewed. Monte Carlo examples are included.
http://www.ssrn.com/abstract=2756199
http://www.ssrn.com/1483274.htmlWed, 30 Mar 2016 11:01:46 GMTREVISION: Simple Nonparametric Estimators for the Bid-Ask Spread in the Roll ModelWe propose new methods for estimating the bid-ask spread from observed transaction prices alone. Our methods are based on the empirical characteristic function instead of the sample autocovariance function like the method of Roll (1984). As in Roll (1984), we have a closed form expression for the spread, but this is only based on a limited amount of the model-implied identification restrictions. We also provide methods that take account of more identification information. We compare our methods theoretically and numerically with the Roll method as well as with its best known competitor, the Hasbrouck (2004) method, which uses a Bayesian Gibbs methodology under a Gaussian assumption. Our estimators are competitive with Roll's and Hasbrouck's when the latent true fundamental return distribution is Gaussian, and perform much better when this distribution is far from Gaussian. Our methods are applied to the E-mini futures contract on the S\&P 500 during the Flash Crash of May 6, 2010. ...
http://www.ssrn.com/abstract=2748858
http://www.ssrn.com/1480090.htmlFri, 18 Mar 2016 08:49:50 GMTNew: Sieve Semiparametric Two-Step GMM Under Weak DependenceThis paper considers semiparametric two-step GMM estimation and inference with weakly dependent data, where unknown nuisance functions are estimated via sieve extremum estimation in the first step. We show that although the asymptotic variance of the second-step GMM estimator may not have a closed form expression, it can be well approximated by sieve variances that have simple closed form expressions. We present consistent or robust variance estimation, Wald tests and Hansen's (1982) over-identification tests for the second step GMM that properly reflect the first-step estimated functions and the weak dependence of the data. Our sieve semiparametric two-step GMM inference procedures are shown to be numerically equivalent to the ones computed as if the first step were parametric. A new consistent random-perturbation estimator of the derivative of the expectation of the non-smooth moment function is also provided.
http://www.ssrn.com/abstract=2630224
http://www.ssrn.com/1411774.htmlTue, 14 Jul 2015 07:12:02 GMT