A Computational Implementation of GMM

GMM 的计算实现

• 批准号: 1459975
• 负责人: Han Hong
• 金额: $ 18.3万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1459975&HistoricalAwards=false
• 关键词: Computational; Implementation; GMM

The PI requests funds for research in econometrics that will develop new methods for data analysis. Many theories developed in economics (and other social and behavioral sciences) predict complicated relationships between variables of interest, relationships that do not fit simple linear regression models. Testing these models requires statistical estimation of non-linear models. In general, estimating economic models of this kind involves a complicated numerical optimization problem. This optimization problem is often combined with the use of numerical simulations (for example, as in maximum simulated likelihood estimation). The result is an extremely complicated computational problem. Computational constraints limit the size of datasets and also limit the kinds of models that can be estimated. The PI seeks to develop methods that are not limited in these ways. This project promotes the progress of science because we must use statistical methods to test hypotheses in many circumstances.The PI and others have proposed ideas that combine simulation with nonparametric regression as a way to reduce the computational problem. Here he proposes to use those ideas to study a statistical method of implementing quasi-Bayes estimators for nonlinear and nonseparable GMM (generalized method of moments) models. He will study both kernel and local polynomial methods and allow for both exact and over identification. The project will also demonstrate the asymptotic validity of inference based on simulated posterior quantile regression. The PI will study the combination with sieve and bootstrap methods.

PI要求为计量经济学研究提供资金，以开发新的数据分析方法。在经济学（以及其他社会和行为科学）中发展起来的许多理论预测了感兴趣的变量之间的复杂关系，这些关系不适合简单的线性回归模型。检验这些模型需要对非线性模型进行统计估计。一般来说，这类经济模型的估计涉及到一个复杂的数值优化问题。此优化问题通常与数值模拟的使用相结合（例如，在最大模拟似然估计中）。其结果是一个极其复杂的计算问题。计算约束限制了数据集的大小，也限制了可以估计的模型的种类。PI寻求开发不受这些方面限制的方法。这个项目促进了科学的进步，因为我们必须在许多情况下使用统计方法来检验假设。PI和其他人提出了将模拟与非参数回归相结合的想法，以减少计算问题。在这里，他建议使用这些思想来研究一种实现非线性和不可分GMM（广义矩法）模型的拟贝叶斯估计的统计方法。他将研究核和局部多项式方法，并允许精确和过度识别。该项目还将展示基于模拟后验分位数回归的推理的渐近有效性。PI将研究筛法和自举法的结合。