Tracking U.S. GDP in Real Time

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Jaeheung Bae, Tae-Yong Doh
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引用次数: 2

Abstract

Measuring the current state of the U.S. economy in real time is an important but challenging task for monetary policymakers. The most comprehensive measure of the state of the economy?real gross domestic product?is available at a relatively low frequency (quarterly) and with a significant delay (one month). To obtain more timely assessments of the state of the economy, the Federal Reserve Bank of Kansas City has developed a GDP tracking model that combines new econometric methods with two conventional approaches to estimating GDP. {{p}} Taeyoung Doh and Jaeheung Bae review the Kansas City Fed model?s underlying details and illustrate its performance by comparing the model?s tracking estimates to those from other real-time tracking models. Their results suggest the Kansas City Fed model provides a useful tool for policymakers by combining estimates and forecasts from factor and accounting-based models.
实时跟踪美国GDP
对货币政策制定者来说,实时衡量美国经济的现状是一项重要但具有挑战性的任务。衡量经济状况最全面的指标是什么?实际国内生产总值?以相对较低的频率(每季度)和明显的延迟(一个月)提供。为了获得对经济状况更及时的评估,堪萨斯城联邦储备银行开发了一种GDP跟踪模型,该模型将新的计量经济学方法与两种传统的估算GDP的方法相结合。{{p}} dotaeyoung和Jaeheung Bae回顾堪萨斯城联储模式?S的底层细节,并通过比较模型来说明其性能?S跟踪估计与其他实时跟踪模型的估计相比较。他们的研究结果表明,堪萨斯城联邦储备银行的模型结合了来自因子模型和基于会计的模型的估计和预测,为政策制定者提供了一个有用的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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