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  • 统计与管理学院2016年学术报告第31

     

    【主  题】MCMC Confidence Sets for Identified Sets

    【报告人】 Chen Xiaohong教授

    Yale University

    【时  间】 2016年6月21日(星期二)14:30-15:30

    【地  点】 上海财经大学统计与管理学院大楼1208室

    【摘  要】In 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 increases, and that they coincide with Bayesian credible sets based on inverting a LR statistic for point-identified likelihood models. We also show that our Monte Carlo optimal criterion-based CSs are uniformly valid over a class of data generating processes that include both partially- and point- identified models. We demonstrate good finite sample coverage properties of our proposed methods in four non-trivial simulation experiments: missing data, entry game with correlated payoff shocks, Euler equation and finite mixture models. Two empirical illustrations using trade data and airline entry data are presented.

    【邀请人】 周勇