经济学和金融从业者的动态模型平均：eDMA包
Dynamic Model Averaging for Practitioners in Economics and Finance: The eDMA Package
摘要：
Raftery、Karny和Ettler（2010）介绍了一种估计技术，他们将其称为动态模型平均（DMA）。在他们的应用中，DMA用于预测冷轧机的输出带材厚度，其中输出是以时间延迟测量的。最近，DMA在宏观经济和金融应用中也显示出有用。在本文中，我们提出了eDMA包的DMA估计实现R。eDMA软件包特别适合经济和金融领域的从业人员，因为这些领域通常有大量的预测指标。我们的实现比使用单核CPU的标准实现快133倍。因此，在此软件包的帮助下，从业者能够在标准PC上执行DMA，而无需求助于大型集群，这对所有研究人员来说都是不容易的。我们通过模拟实验和实证应用程序，使用季度美国通货膨胀数据证明了这个包的有用性。
英文摘要：
Raftery, Karny, and Ettler (2010) introduce an estimation technique, which they refer to as Dynamic Model Averaging (DMA). In their application, DMA is used to predict the output strip thickness for a cold rolling mill, where the output is measured with a time delay. Recently, DMA has also shown to be useful in macroeconomic and financial applications. In this paper, we present the eDMA package for DMA estimation implemented in R. The eDMA package is especially suited for practitioners in economics and finance, where typically a large number of predictors are available. Our implementation is up to 133 times faster then a standard implementation using a single-core CPU. Thus, with the help of this package, practitioners are able to perform DMA on a standard PC without resorting to large clusters, which are not easily available to all researchers. We demonstrate the usefulness of this package through simulation experiments and an empirical application using quarterly U.S. inflation data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
