Toward Accurate Inference in Nonlinear Dynamic Models

9320370 Gallant该项目开发了不依赖于 正确规范经济模型的结构， 其有效性。 这是一个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 大规格错误。 其基本思想是取代 结构模型的近似，逐步提高 随着更多的信息变得可用。 开发的方法 该项目允许在每个中间阶段进行可靠推断 模型进化。 这些程序足够通用， 包含大多数计量经济学推理程序。 下 研究人员成功地开发了 统计方法具有某些优点， 与计量经济学应用有关：简单、易于 实现和易于扩展到非线性、多变量 时间序列应用。 根据这项赠款，这些方法 将扩展到动态设置，并用于分析 金融市场 与过去一样，实现 理论和实证工作将被编码和记录， 专业编写的科学软件的现行标准 然后放到公共领域 在该项目下开发的方法被称为 单参数（SNP）方法，因为它们是参数化的， 具有非参数特性。 这些过程取代了 结构模型与截断级数展开，误差 密度与截断扩展，或两者。 通过让 截断随样本大小自适应增长，近似为 在每个中间阶段都足够精确， 推理和最终收敛到底层数据 生成机制 申请人须有条件地 异构时间序列，如发生在金融和 宏观经济学 贝叶斯方法也进行了研究。 ***

9320370 Gallant 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 th e underlying data generating mechcanism. Applications are made to conditionally heterogeneous time series such as occur in finance and macroeconomics. Bayesian methods are also studied. ***