Toward Accurate Inference in Nonlinear Dynamic Models

实现非线性动态模型的准确推理

• 批准号: 9111867
• 负责人: A. Ronald Gallant
• 金额: $ 10.68万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-04-01 至 1993-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9111867&HistoricalAwards=false
• 关键词: Toward; Accurate; Inference; Nonlinear; Dynamic

This project develops econometric methods that do not rely on the correct specification of the structure of economic models for their validity. This is an extremely important line of research because these methods permit empirical research to concentrate on drawing economic inferences from data without risking potentially large specification errors. The basic idea is to replace the structural model with an approximation that sequentially improves as more information becomes available. The methods developed by this project permit reliable inference at each intermediate stage of model evolution. The procedures are general enough to encompass most econometric inference procedures. Under a previous grant the investigator succeeded in developing statistical methods that have certain advantages particularly relevant to econometric applications: simplicity, ease of implementation, and ease of extension to nonlinear, multivariate, and time series applications. Under this grant these methods will be extended to a dynamic setting and used to analyze financial markets. As in the past, algorithms implementing the theoretical and empirical work will be coded and documented to current standards for professionally written, scientific software and put in the public domain. The methods developed under this project are termed seminonparametric (SNP) methods because they are parametric yet have nonparametric properties. The procedures replace the structure models with a truncated series expansion, the error density with a truncated expansion, or both. By letting the truncation grow adaptively with sample size, the approximation is accurate enough at each intermediate stage to permit reliable inference and ultimate convergence to the underlying data generating mechanism. Applications are made to conditionally heterogeneous time series such as occur in finance and macroeconomics. Bayesian methods are also studied.

该项目开发了计量经济学方法，这些方法不依赖于经济模型的有效性的正确规范。 这是一项极为重要的研究线，因为这些方法允许实证研究集中于从数据中汲取经济推论，而不会冒着潜在的大规范错误。 基本思想是用近似值替换结构模型，该模型随着更多信息的可用性而顺序改进。 该项目开发的方法允许在模型演化的每个中间阶段可靠的推断。 该程序足够笼统地涵盖了大多数计量经济学推理程序。 根据先前的赠款，研究人员成功地开发了具有某些优势与计量经济学应用相关的某些优势的统计方法：简单，易于实施以及易于扩展非线性，多元和时间序列应用程序。 根据这项赠款，这些方法将扩展到动态环境，并用于分析金融市场。 与过去一样，实施理论和经验工作的算法将被编码并记录在当前的专业编写，科学软件的标准中，并放入公共领域。 在该项目下开发的方法称为seminonmotetric（SNP）方法，因为它们是参数性但具有非参数属性的。 这些过程用截断的串联膨胀，误差密度用截断的扩展或两者兼而有之。 通过让截短以样本量的自适应生长，在每个中间阶段的近似值足够准确，以允许可靠的推理和最终收敛到基础数据生成机制。 应用于有条件的异质时间序列，例如金融和宏观经济学中的发生。 还研究了贝叶斯方法。