Business Surveys Modelling with Seasonal-Cyclical Long Memory Models

L. Ferrara, D. Guégan
{"title":"Business Surveys Modelling with Seasonal-Cyclical Long Memory Models","authors":"L. Ferrara, D. Guégan","doi":"10.2139/ssrn.1678415","DOIUrl":null,"url":null,"abstract":"Business surveys are an important element in the analysis of the short-term economic situation because of the timeliness and nature of the information they convey. Especially, surveys are often involved in econometric models in order to provide an early assessment of the current state of the economy, which is of great interest for policy-makers. In this paper, we focus on non-seasonally adjusted business surveys released by the European Commission. We introduce an innovative way for modelling those series taking the persistence of the seasonal roots into account through seasonal-cyclical long memory models. We empirically prove that such models produce more accurate forecasts than classical seasonal linear models.","PeriodicalId":101534,"journal":{"name":"Banque de France Research Paper Series","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2008-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Banque de France Research Paper Series","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.1678415","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13

Abstract

Business surveys are an important element in the analysis of the short-term economic situation because of the timeliness and nature of the information they convey. Especially, surveys are often involved in econometric models in order to provide an early assessment of the current state of the economy, which is of great interest for policy-makers. In this paper, we focus on non-seasonally adjusted business surveys released by the European Commission. We introduce an innovative way for modelling those series taking the persistence of the seasonal roots into account through seasonal-cyclical long memory models. We empirically prove that such models produce more accurate forecasts than classical seasonal linear models.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
商业调查建模与季节性-周期性长记忆模型
商业调查是分析短期经济形势的一个重要因素,因为它们所传达的信息具有及时性和性质。特别是,为了提供对当前经济状况的早期评估,调查经常涉及计量经济模型,这是政策制定者非常感兴趣的。在本文中,我们关注欧盟委员会发布的非季节性调整的商业调查。我们引入了一种创新的方法来建模这些系列,通过季节性周期长记忆模型考虑到季节性根的持久性。我们的经验证明,这种模型比经典的季节性线性模型产生更准确的预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Web Scraping Housing Prices in Real-time: the Covid-19 Crisis in the UK Firms’ Inflation Expectations: New Evidence from France Does one (Unconventional) Size Fit All? Effects of the ECB's Unconventional Monetary Policies on the Euro Area Economies Downward Interest Rate Rigidity Inflation tolerance ranges in the New Keynesian model
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1