Toward Accurate Inference in Nonlinear Dynamic Models

9320370盖朗特这个项目开发了计量经济学方法，这些方法不依赖于经济模型结构的正确说明来保证其有效性。这是一条极其重要的研究路线，因为这些方法允许实证研究专注于从数据中得出经济推论，而不会冒着潜在的巨大规格错误的风险。其基本思想是用近似值替换结构模型，该近似值随着可用信息的增多而不断改进。该项目开发的方法允许在模型演变的每个中间阶段进行可靠的推断。这些程序足够普遍，足以涵盖大多数计量经济学推论程序。在之前的一次拨款中，研究人员成功地开发了统计方法，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，并且易于扩展到非线性、多变量和时间序列应用。根据这笔赠款，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，执行理论和经验工作的算法将按照专业编写的科学软件的当前标准进行编码和记录，并投入公共领域。在这个项目下开发的方法被称为半参数(SNP)方法，因为它们是参数的，但具有非参数性质。这些程序用截断级数展开代替结构模型，用截断展开代替误差密度，或者两者兼而有之。通过让截断随样本量自适应地增长，在每个中间阶段的近似足够精确，以允许可靠的推断和最终收敛到潜在的数据产生机制。将其应用于金融和宏观经济中出现的条件异质性时间序列。并对贝叶斯方法进行了研究。***

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. ***