Applied Dynamic Macroeconomics

应用动态宏观经济学

• 批准号: 8808121
• 负责人: Thomas Sargent
• 金额: $ 14.7万
• 依托单位: National Bureau of Economic Research Inc
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1988
• 资助国家: 美国
• 起止时间: 1988-08-01 至 1992-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8808121&HistoricalAwards=false
• 关键词: Applied; Dynamic; Macroeconomics

This project investigates a variety of topics in dynamic macroeconomics. The topics basically fall into two categories: (1) linear time series analyses of rational expectations models and (2) the micro foundations of monetary theory. Anyone who has dealt with aggregate U.S. time series data understands the potential for measurement error to bias the analysis in quantitatively significant ways. This is obviously true in structural linear rational expectations models where the sources of error terms in reduced form representations of the data are critical to the analysis. It is also central to innovation accounting exercises of the type undertaken by Sims, as well as analyses of the Blanchard-Watson type which focus on exactly identified VAR systems. The proposed work on interpreting seasonality, alternative models of measurement error, and least-squares learning in multi-agent settings is motivated by a desire to increase the econometric applicability of dynamic economic models and improve their interpretation, absent a more fundamental solution which would generate better measured data. The two studies on monetary theory, similarly, aim to deepen the understanding of different policy predictions given by alternative monetary models. The particular focus of this research is on the impact of timing conventions and communications technologies. Although each of the several sub-projects deals with different aspects of dynamic macroeconomics and is independent of the others, in combination their results should contribute to strengthening the general underlying theory. At the same time, the findings on seasonality and measurement error will have substantial implications for the way econometricians use seasonally adjusted data and interpret empirical results derived from that data.

这个项目调查了动态中的各种主题 宏观经济学 这些主题基本上分为两类： (1)理性预期模型的线性时间序列分析 （2）货币理论的微观基础。 任何处理过美国时间序列数据的人 了解测量误差可能会使 以定量的方式进行分析。 这显然是 在结构线性理性预期模型中， 的简化形式表示中的误差项的来源 数据对分析至关重要。 它也是中央， 西姆斯进行的那种创新会计练习， 以及布兰查德-沃森类型的分析， 准确识别VAR系统。 拟议的工作 解释季节性，替代测量模型 错误，多智能体设置中的最小二乘学习是 出于增加计量经济学适用性的愿望， 动态经济模型，并改善其解释， 缺乏一个更根本的解决方案， 测量数据。 这两项关于货币理论的研究，同样， 旨在加深对不同政策预测的理解 替代货币模型。 特别关注 这项研究是关于时间约定的影响， 通信技术。 虽然几个子项目中的每一个涉及不同的 动态宏观经济学的各个方面，独立于 其他人，结合他们的成果，应有助于 加强一般基础理论。 与此同时， 关于季节性和测量误差的调查结果将有 对计量经济学家使用 季节性调整的数据和解释得出的经验结果 from that data数据.