AN OVERVIEW OF DYNAMIC MODEL AVERAGING TECHNIQUES IN TIME‐SERIES ECONOMETRICS
综述了动态模型平均（DMA）技术自2009年引入计量经济学以来的发展，包括其在通胀率预测、股票收益率预测等宏观经济应用中的扩展，以及如何结合谷歌搜索数据或大规模时变参数向量自回归模型提升预测精度。
Abstract Dynamic model averaging (DMA) has become a widely used estimation technique in macroeconomic applications. Since its introduction in econom(etr)ics by Gary Koop and Dimitris Korobilis in 2009, applications of DMA have increased in unimaginable ways. Besides applying the original (univariate) framework suggested by Koop and Korobilis on the data of interest, for example, the inflation rate of the country of choice or return on the rate of equity, practitioners have been able to use DMA‐based techniques to extend current models, thereby further improving out‐of‐sample forecast accuracy, overcome computational bottlenecks, and even help improve our understanding of economic phenomena by introducing new models. These include using Google search data in combination with the predictive likelihood to govern switching between different predictive regressions in the model set or specifying large time‐varying parameter vector autoregressions that can be estimated without resorting to simulation‐based techniques. This study provides an overview of DMA techniques and the ways in which they have evolved since the contribution of Koop and Korobilis.