Computationally Intensive Strategies for Structural Modelling

结构建模的计算密集型策略

• 批准号: 0438174
• 负责人: A. Ronald Gallant
• 金额: --
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-01 至 2008-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0438174&HistoricalAwards=false
• 关键词: Computationally; Intensive; Strategies; Structural; Modelling

This proposal requests continued support for a program of research in nonlinear econometric methods. The motivation for this line of research is that econometric inference regarding the ideas, theories, and models from economics should be made without compromise to the discipline: Economic models should not be simplified or crudely approximated to accommodate limits imposed by econometric theory.Research under the previous grant focused on estimation of parametric and semiparametric structural models that are so complex that they can only be estimated by simulation methods. The idea was to require that moments determined by simulation from the structural model match the scores of a certain truncation sieve. This estimator is as efficient as maximum likelihood would be were it feasible and is termed efficient method of moments or EMM for that reason. Diagnostics based on the scores that can pinpoint the reasons for model failure were developed. These methods work well when data is abundant such as financial econometrics. When applied in areas where data is sparse, such as macroeconomics, the maximum permissible truncation point becomes so small that the diagnostics cannot detect a model's inability to track important features of the data such as conditional scale and a claim that passing the diagnostic tests certifies a model's adequacy becomes suspect.This award supports the development of methods similar to EMM that work well when data is sparse. The most promising is a Bayesian approach for which the structural model is taken as the prior on a truncation sieve. The advantage is that the prior amounts to a dimensionality reduction that makes computations feasible when data is sparse. Relaxation of the prior leads to informative diagnostics. The Markov chain Monte Carlo computational strategy developed for this Bayesian estimator can also be applied to frequentist estimators. Because it is more robust than traditional quasi Newton hill climbing methods, estimation using nonstandard criterion functions becomes practicable. The Cramer-Von Mises criterion is an instance that is interesting because it is defensible when the economic model is admittedly misspecified and it gives results that seem qualitatively similar to the Bayesian approach in applications. A theoretical justification of Cramer-Von Mises estimates is sought. This project also continues an ongoing program of empirical work that exploits new methodologies developed under this program of econometric research. Initial work will focus on determining whether structural macro models that can pass tests of model adequacy based on macro time series data will continue to do so when required to confront, in addition, data on cash flows from a cross section of assets.Broad impact: The availability of practicable methods to determine from data the parameters of substantive models rich enough to accurately represent target phenomena and the availability of methods to assess the adequacy of such models is of considerable societal importance. The methods developed within the program have been applied in science, business, and government. In thesciences diffusion has been broad; a sample of citations in non-economic and non-statistical journals is the following: American Journal of Epidemiology, American Naturalist, Annals of Human Biology, Ecology, Journal of Pharmacokinetics and Biopharmaceutics, Journal of Psychiatric Research, Nature, Physica D, and Physics and Chemistry of the Earth C. Diffusion to business and government has been primarily through inclusion of methods developed within the project in commercial statistical packages such as SAS.

该提案要求继续支持非线性计量经济学方法的研究计划。这一研究方向的动机是，关于经济学的思想、理论和模型的计量经济学推论应该在不损害学科的情况下进行：经济模型不应被简化或粗略地近似以适应计量经济学理论所施加的限制。之前的资助主要集中于参数和半参数结构模型的估计，这些模型非常复杂，只能通过模拟方法进行估计。这个想法是要求通过结构模型的模拟确定的力矩与特定截断筛的分数相匹配。 如果可行的话，该估计器与最大似然一样有效，因此被称为有效矩法或 EMM。开发了基于分数的诊断方法，可以查明模型失败的原因。当数据丰富（例如金融计量经济学）时，这些方法效果很好。 当应用于数据稀疏的领域（例如宏观经济学）时，最大允许截断点变得如此之小，以至于诊断无法检测到模型无法跟踪数据的重要特征（例如条件尺度）以及通过诊断测试证明了模型的充分性受到质疑。该奖项支持开发类似于 EMM 的方法，这些方法在数据稀疏时效果良好。最有前途的是贝叶斯方法，其结构模型被视为截断筛的先验。优点是先验相当于降维，使得在数据稀疏时计算变得可行。放宽先验导致信息丰富的诊断。为该贝叶斯估计器开发的马尔可夫链蒙特卡罗计算策略也可以应用于频率估计器。由于它比传统的拟牛顿爬山方法更稳健，因此使用非标准准则函数进行估计变得可行。克拉默-冯·米塞斯准则是一个有趣的例子，因为当经济模型被承认错误指定时，它是有道理的，并且它给出的结果在质量上与应用中的贝叶斯方法相似。 寻求克拉默-冯·米塞斯估计的理论依据。 该项目还继续进行一项正在进行的实证工作计划，利用根据该计量经济学研究计划开发的新方法。 初步工作将侧重于确定能够通过基于宏观时间序列数据的模型充分性测试的结构性宏观模型，当需要另外面对来自资产横截面的现金流量数据时，是否会继续这样做。广泛影响：从数据中确定足够丰富的实质性模型参数以准确表示目标现象的实用方法的可用性以及评估此类模型的充分性的方法的可用性具有相当大的社会重要性。该计划开发的方法已应用于科学、商业和政府。在科学领域，传播范围很广。非经济和非统计期刊的引文样本如下：《American Journal of Epidemiology》、《American Naturalist》、《Annals of Human Biology》、《Ecology》、《Journal of Pharmacokinetics and Biopharmaceutics》、《Journal of Psychiatric Research》、《Nature》、《Physica D》和《Physics》向企业和政府的传播主要是通过将项目中开发的方法纳入商业统计包（例如 SAS）中。