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.

中文翻译：

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时序经济中的动态模型平均技术概述

动态模型平均（DMA）已成为宏观经济应用中广泛使用的估算技术。自2009年Gary Koop和Dimitris Korobilis将其引入经济学以来，DMA的应用以不可思议的方式增长。除了将Koop和Korobilis建议的原始（单变量）框架应用于感兴趣的数据（例如，所选国家的通货膨胀率或股本回报率）外，从业人员还能够使用基于DMA的技术来扩展当前模型，从而进一步提高了样本外预测的准确性，克服了计算瓶颈，甚至通过引入新模型帮助提高了我们对经济现象的理解。其中包括将Google搜索数据与预测可能性结合使用，以控制模型集中不同预测回归之间的切换，或指定无需借助基于仿真的技术即可估算的时变参数矢量自回归。这项研究概述了DMA技术以及自Koop和Korobilis的贡献以来它们发展的方式。