Applied Dynamic Macroeconomics

这个项目调查了动态宏观经济学的各种主题。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。任何处理过美国总体时间序列数据的人都知道，测量误差可能会在数量上显著地影响分析。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。它也是SIMS进行的那种创新会计练习的核心，以及对Blanchard-Watson类型的分析，这些分析侧重于准确识别的VAR系统。拟议在多主体环境下解释季节性、衡量误差的替代模型和最小二乘学习的工作，是出于增加动态经济模型的计量经济学适用性并改善其解释的愿望，而缺乏能够产生更好测量数据的更根本的解决方案。同样，这两项关于货币理论的研究旨在加深对替代货币模型给出的不同政策预测的理解。这项研究的重点是计时惯例和通信技术的影响。尽管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，并且独立于其他方面，但结合起来，它们的结果应该有助于加强一般基础理论。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经季节性调整的数据和解释从这些数据得出的经验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.