摘要翻译：
在复杂/非线性参数模型中，通常很难知道模型参数是否是点辨识的。我们提供了计算上有吸引力的方法来构造模型中通过矩等式或不等式的似然或向量定义的全参数和子向量的识别集的置信集。这些CSs基于最优样本准则函数的水平集（例如似然或最优加权或持续更新的GMM准则）。水平集是用直接从准则的准后验分布通过蒙特卡罗(MC)模拟计算的截止值来构造的。对于部分辨识的正则模型和一些非正则模型中的拟似然比(QLR)和轮廓QLR的拟后验分布，我们建立了新的Bernstein-von Mises（或Bayesian Wilks）型定理。这些结果表明，在部分辨识的正则模型中，我们的MC CSs对已辨识的全参数集和子向量集具有精确的渐近频率复盖，在边界上含有约简形参数的模型中，我们的MC CSs具有有效但潜在保守的复盖。在具有奇异性的模型中，我们的子向量辨识集的MC CSs具有精确的渐近覆盖。我们还提供了CSs在包括点和部分识别模型的DGPs类上的一致性有效性的结果。我们在两个模拟实验中证明了我们的程序良好的有限样本覆盖特性。最后，我们的程序应用于两个非平凡的实证例子：一个航空公司进入博弈和一个贸易流动模型。
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英文标题：
《Monte Carlo Confidence Sets for Identified Sets》
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作者：
Xiaohong Chen, Timothy Christensen and Elie Tamer
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最新提交年份：
2017
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分类信息：

一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Mathematics        数学
二级分类：Statistics Theory        统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类：Statistics        统计学
二级分类：Statistics Theory        统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
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英文摘要：
  In 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 sets of full parameters and of subvectors in partially-identified regular models, and have valid but potentially conservative coverage in models with reduced-form parameters on the boundary. Our MC CSs for identified sets of subvectors are shown to have exact asymptotic coverage in models with singularities. We also provide results on uniform validity of our CSs over classes of DGPs that include point and partially identified models. We demonstrate good finite-sample coverage properties of our procedures in two simulation experiments. Finally, our procedures are applied to two non-trivial empirical examples: an airline entry game and a model of trade flows.
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PDF链接：
https://arxiv.org/pdf/1605.00499