Collaborative Research: A Markov Chain Approach to Classical Estimation

协作研究：经典估计的马尔可夫链方法

• 批准号: 0335113
• 负责人: Han Hong
• 金额: $ 8.69万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-07-01 至 2004-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0335113&HistoricalAwards=false
• 关键词: Collaborative; Research; Markov; Chain; Approach

This project develops the statistical properties of a class of new estimators, called quasi-Bayesian (QBE) or Laplacian estimators. These estimators are applicable to highly nonlinear classical M estimation problems, and to many non-smooth semiparametric estimation problems. They use the latest development in Monte Carlo Markov Chain simulations in Bayesian statistics to overcome the computational difficulty of many nonlinear classical estimators. The results of this research project contribute to the understanding of computation and inference in general nonlinear econometric models. Computation and inference are two inseparable essential parts of any econometric model. The proposed estimators address both issues and can potentially be used extensively in empirical work. Many parametric and semiparametric estimators involve non-convex and nonsmooth objective functions. This not only makes proving large sample statistical properties difficult, but also makes the estimators difficult, if possible, to compute in practice. The first part of this project defines quasi-Bayesian estimators that aim at overcoming these two prominent issues, and studies their regular consistency and asymptotic normality properties. When the underlying objective function is an actual log likelihood function of the data, the QBEs reduce to the usual Bayesian approach. The second part studies the properties of likelihood based inferences, including Bayesian and maximum likelihood estimators, in a class of nonregular structural econometric models, in which the support of the dependent variable can depend on both the model parameters and the independent covariates. These models arise naturally in the context of structural auction models, empirical equilibrium job search models and frontier production analysis. This part of the project provides a unified treatment of likelihood based estimation and inference in these models. The first two parts apply to finite dimensional parameters. The third part extends the quasi-Bayesian approach to estimate finite and infinite dimensional parameters simultaneously, in which the infinite dimensional parameter is approximated by a sieve space. The quasi-Bayesian estimator overcomes the computational obstacle of sieve semiparametric models. The fourth project analyzes the properties of QBEs when the parameters are on the boundary of the parameter space, which can be defined through linear or nonlinear constraints on the parameters. While the properties of M estimators have been extensively analyzed in the literature, they do not share the optimality properties of M estimators when the parameter is in the interior of the parameter space. QBEs provide useful alternative optimal estimators, and enjoy tractable computational and inference properties. The fifth part focuses on the computational properties of QBEs and the implementation details for a range of specific problems. It studies convergence criteria that are used to monitor the computation of QBEs and improvements to the popular generic optimization algorithm simulated annealing, by replacing the generic Metropolis step with other Gibbs sampling algorithms.

该项目开发了一类新估计量的统计特性，称为准贝叶斯（QBE）或拉普拉斯估计量。这些估计适用于高度非线性的经典M估计问题，以及许多非光滑半参数估计问题。 他们使用贝叶斯统计中蒙特卡罗马尔可夫链模拟的最新发展来克服许多非线性经典估计的计算困难。本研究的结果有助于理解一般非线性计量经济模型中的计算和推理。计算和推理是任何计量经济学模型不可分割的两个基本部分。建议的估计解决这两个问题，并可能被广泛用于实证工作。许多参数和半参数估计涉及非凸和非光滑的目标函数。这不仅使得证明大样本统计性质变得困难，而且使得估计量在可能的情况下难以在实践中计算。本计画的第一部分定义了拟贝氏估计量，以克服这两个突出的问题，并研究其正则相合性与渐近正态性。当潜在的目标函数是数据的实际对数似然函数时，QBE减少到通常的贝叶斯方法。第二部分研究了一类非正则结构计量经济模型中基于似然的推断（包括贝叶斯估计和极大似然估计）的性质，其中因变量的支持度既依赖于模型参数，也依赖于独立协变量。这些模型自然出现在结构拍卖模型，实证均衡求职模型和前沿生产分析的背景下。项目的这一部分提供了这些模型中基于似然的估计和推断的统一处理。前两部分适用于有限维参数。 第三部分将拟贝叶斯方法推广到同时估计有限维和无限维参数，其中无限维参数用一个筛空间来近似。拟贝叶斯估计克服了半参数模型的计算困难。 第四个项目分析了当参数位于参数空间的边界上时QBE的性质，参数空间可以通过对参数的线性或非线性约束来定义。虽然M估计的性质在文献中已经被广泛分析，但当参数在参数空间的内部时，它们不具有M估计的最优性性质。QBE提供了有用的替代最优估计，并享有易处理的计算和推理性能。第五部分着重于QBE的计算特性和一系列具体问题的实现细节。它研究了用于监控QBE计算的收敛标准，并通过用其他Gibbs采样算法代替通用大都会步骤来改进流行的通用优化算法模拟退火。