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.

该项目开发的计量经济学方法的有效性不依赖于经济模型结构的正确规范。 这是一条极其重要的研究方向，因为这些方法允许实证研究集中于从数据中得出经济推论，而不会冒潜在的巨大规范错误的风险。 基本思想是用近似值替换结构模型，随着更多信息的可用，该近似值会依次改进。 该项目开发的方法允许在模型演化的每个中间阶段进行可靠的推理。 这些程序足够通用，足以涵盖大多数计量经济学推理程序。 在先前的资助下，研究人员成功开发了统计方法，这些方法具有与计量经济学应用特别相关的某些优势：简单、易于实施以及易于扩展到非线性、多元和时间序列应用。 根据这笔拨款，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，实现理论和实证工作的算法将按照专业编写的科学软件的现行标准进行编码和记录，并置于公共领域。 该项目开发的方法称为半非参数 (SNP) 方法，因为它们是参数化的，但具有非参数属性。 该过程用截断级数展开替换结构模型，用截断展开替换误差密度，或者两者兼而有之。 通过让截断随着样本大小自适应增长，近似在每个中间阶段都足够准确，以允许可靠的推理和最终收敛到底层数据生成机制。 应用到条件异构时间序列，例如金融和宏观经济学中的时间序列。 还研究了贝叶斯方法。