Applied Dynamic Macroeconomics

这个项目调查了动态宏观经济学中的各种主题。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和(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都知道，测量误差可能会在定量上显著影响分析结果。这在结构线性理性预期模型中显然是正确的，其中数据的简化形式表示的误差项的来源对分析至关重要。这也是西姆斯所从事的创新会计实践的核心，也是布兰查德-沃森类型分析的核心，该分析专注于准确识别的VAR系统。在没有更基本的解决方案来产生更好的测量数据的情况下，对多智能体设置中解释季节性、测量误差的替代模型和最小二乘学习的建议工作的动机是希望增加动态经济模型的计量经济学适用性并改进其解释。同样，这两项关于货币理论的研究旨在加深对不同货币模型给出的不同政策预测的理解。本研究的重点是定时约定和通信技术的影响。虽然这几个子项目中的每一个都涉及动态宏观经济学的不同方面，并且彼此独立，但结合起来，它们的结果应该有助于加强一般的基础理论。同时，关于季节性和测量误差的发现将对计量经济学家使用季节性调整数据和解释从该数据得出的实证结果的方式产生重大影响。

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.