Modelling Economic Time Series Under A Bayesian Frame of Reference

提出了分析经济时间序列的新方法 使用时间序列分析和半参数的贝叶斯方法 协整系统中的规范测试。 实证 这些方法的应用包括分析数据， 美国经济的宏观经济时间序列， 韩国、澳大利亚和新西兰的宏观经济数据， 长期股价和股息序列。 大量的模拟 进行实验以评估新的性能。 程序. 本项目开发的新估算方法 应该提高实证经济研究的质量， 范围广泛的问题。 这项工作特别及时，因为 最近对应用贝叶斯方法的兴趣增加， 本项目所使用的框架，以实证经济研究。 该项目的主要活动涉及目标 时间序列分析的贝叶斯方法。 特别注意的是 经济时间序列的行为表明， 非平稳特性 确定基于模型的 适应非平稳的参考先验将是 仔细考虑。 数据条件作用在 贝叶斯时间序列分析是人们关注的主要焦点。 在上一个赠款中开发的概念框架是 扩展到贝叶斯模型似然检验、后验概率检验 和模型选择标准。 型号选择标准 提供了一个广泛使用的标准的概括。 所有 贝叶斯的这些特征 推理进行了详细探讨，渐近理论， 开发了一个一般类的时间序列问题。 协整中的半参数规格检验 依赖于拉格朗日乘子（LM）原理。 的lm 方法提供了一个长期的模型规格测试 结构系统的组成部分，并对两者进行测试 低规格（太少的长期关系）和 过度规范（太多的长期关系）。 两个部分 该项目将通过开发贝叶斯模型 规范测试，在协整系统的情况下， 将与LM测试程序密切相关。

New procedures for analyzing economic time series are developed using Bayesian methods of time series analysis and semiparametric specification testing in cointegrated systems. Empirical applications of these methods include analysis of data on macroeconomic time series for the United States economy, macroeconomic data for Korea, Australia and New Zealand, and some long stock price and dividend series. Extensive simulation experiments are conducted to evaluate the performance of the new procedures. The new estimation methods developed by this project should improve the quality of empirical economic research on a wide range of problems. The work is especially timely because of the recent increase in interest in applying Bayesian methods, the framework used by this project, to empirical economic research. The main activity of the project is concerned with objective Bayesian methods of time series analysis. Specific attention is given to economic time series whose behavior indicates possible nonstationary characteristics. Issues of determining model-based reference priors that accommodate nonstationary will be considered in detail. The effects of data conditioning in Bayesian time series analysis is the major focus of attention. The conceptual framework developed in the previous grant is extended to Bayes model likelihood tests, posterior odds tests and model selection criteria. The model selection criteria provides a generalization of a widely used criterion. All of these features of Bayesian inference are explored in detail and an asymptotic theory is developed for a general class of time series problems. The work on semiparametric specification testing in cointegration relies on the Lagrange multiplier (LM) principle. The LM approach delivers a model specification test for the long-run elements of a structural system and tests against both underspecification (too few long-run relations) and overspecification (too many long-run relations). The two parts of the project will be related by developing a Bayes model specification test which, in the case of cointegrated systems, will be closely related to the LM test procedure.