专业预测师到底预测了什么?

D. Nibbering, R. Paap, Michel van der Wel
{"title":"专业预测师到底预测了什么?","authors":"D. Nibbering, R. Paap, Michel van der Wel","doi":"10.2139/ssrn.2640878","DOIUrl":null,"url":null,"abstract":"In this paper we study what professional forecasters predict. We use spectral analysis and state space modeling to decompose economic time series into a trend, business-cycle, and irregular component. To examine which components are captured by professional forecasters, we regress their forecasts on the estimated components extracted from both the spectral analysis and the state space model. For both decomposition methods we find that the Survey of Professional Forecasters in the short run can predict almost all variation in the time series due to the trend and business-cycle, but the forecasts contain little or no significant information about the variation in the irregular component.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"What Do Professional Forecasters Actually Predict?\",\"authors\":\"D. Nibbering, R. Paap, Michel van der Wel\",\"doi\":\"10.2139/ssrn.2640878\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we study what professional forecasters predict. We use spectral analysis and state space modeling to decompose economic time series into a trend, business-cycle, and irregular component. To examine which components are captured by professional forecasters, we regress their forecasts on the estimated components extracted from both the spectral analysis and the state space model. For both decomposition methods we find that the Survey of Professional Forecasters in the short run can predict almost all variation in the time series due to the trend and business-cycle, but the forecasts contain little or no significant information about the variation in the irregular component.\",\"PeriodicalId\":308524,\"journal\":{\"name\":\"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)\",\"volume\":\"69 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.2640878\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.2640878","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

在本文中,我们研究了专业预测者的预测。我们使用谱分析和状态空间建模将经济时间序列分解为趋势、商业周期和不规则成分。为了检查专业预报员捕获了哪些成分,我们将他们的预测回归到从光谱分析和状态空间模型中提取的估计成分上。对于这两种分解方法,我们发现专业预测者调查在短期内几乎可以预测由于趋势和商业周期而导致的时间序列的所有变化,但预测中几乎没有包含关于不规则成分变化的重要信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
What Do Professional Forecasters Actually Predict?
In this paper we study what professional forecasters predict. We use spectral analysis and state space modeling to decompose economic time series into a trend, business-cycle, and irregular component. To examine which components are captured by professional forecasters, we regress their forecasts on the estimated components extracted from both the spectral analysis and the state space model. For both decomposition methods we find that the Survey of Professional Forecasters in the short run can predict almost all variation in the time series due to the trend and business-cycle, but the forecasts contain little or no significant information about the variation in the irregular component.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信