Error-Correction Reinterpretation and Efficient Estimation of Dynamic Stochastic General Equilibrium Models

动态随机一般均衡（DSGE）模型是实证宏观经济学的重要工具，广泛用于政策分析。PI的研究试图在一个更灵活的数学框架内重新解释DSGE模型。我们的目标是提供解决DSGE模型的一些关键缺陷的方法，并为理解商业周期波动和政府政策的影响提供一个更强大的框架。PI计划开发一种在某些方面类似于英格兰银行季度模型的方法，其中稳态核心解决方案嵌入在使用去趋势数据的误差校正机制（ECM）方程中。 有一个关键的区别; PI计划使用核心解决方案之间的平衡增长（对数）比率作为ECM目标，而不是对数据进行去趋势化并为核心解决方案构建ECM方程。这样做有两个原因。 首先，这些口粮是明显的候选人协整关系，第二，正式占（近）单位根的优势是很好理解的。 它们增强了对关键结构参数的推断，并在分离数据中持久性和不太持久的运动方面发挥了关键作用。 PI还将继续开发适用于ECM/DSGE模型的数字高效过滤技术。

Dynamic Stochastic General Equilibrium (DSGE) models are workhorses of empirical macroeconomics, and are widely used for policy analysis. The PI's research seeks to reinterpret DSGE models within a more flexible mathematical framework. The goal is to provide methods that address some key deficiencies of DSGE models and provide a more robust framework for understanding business cycle fluctuations and the effects of government policies. The PI plans to develop a method that is in some ways similar to the Bank of England Quarterly Model, in which steady state core solutions are embedded within Error Correction Mechanism (ECM) equations using detrended data. There is a key difference; rather than detrending data and constructing ECM equations for core solutions, the PI plans to use as ECM targets balanced growth (log) ratios between core solutions. There are two reasons for proceeding in this way. First, these rations are obvious candidates for co-integrating relationships, and second, the advantages of formally accounting for (near) unit roots are well understood. They robustify inference of key structural parameters and play a key role in separating persistent from less persistent movements in the data. The PI will also continue to develop numerically efficient filtering techniques applicable to ECM/DSGE models.