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

点击翻译按钮获取中文摘要

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.