Intuitive and Reliable Estimates of the Output Gap from a Beveridge-Nelson Filter

Author:

Kamber Güneş1,Morley James2,Wong Benjamin3

Affiliation:

1. Bank for International Settlements and Reserve Bank of New Zealand

2. University of Sydney

3. Monash University, Australia, and Reserve Bank of New Zealand

Abstract

Abstract The Beveridge-Nelson decomposition based on autoregressive models produces estimates of the output gap that are strongly at odds with widely held beliefs about transitory movements in economic activity. This is due to parameter estimates implying a high signal-to-noise ratio in terms of the variance of trend shocks as a fraction of the overall forecast error variance. When we impose a lower signal-to-noise ratio, the resulting Beveridge-Nelson filter produces a more intuitive estimate of the output gap that is large in amplitude and highly persistent, and it typically increases in expansions and decreases in recessions. Notably, our approach is also reliable in the sense of being subject to smaller revisions and predicting future output growth and inflation better than other trend-cycle decompositions that impose a low signal-to-noise ratio.

Publisher

MIT Press - Journals

Subject

Economics and Econometrics,Social Sciences (miscellaneous)

Cited by 86 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Excess capacity and hysteresis in EU Countries. A structural approach;Structural Change and Economic Dynamics;2024-12

2. Business cycle synchronization and asymmetry in the European Union;Economic Modelling;2024-10

3. Tracking trend output using expectations data;Journal of the Royal Statistical Society Series A: Statistics in Society;2024-07-24

4. The stability and economic relevance of output gap estimates;Journal of Applied Econometrics;2024-06-18

5. An aggregation-consistent implementation of the Hamilton filter;Applied Economics Letters;2024-06-06

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3