利用跨国数据预测美国经济衰退中的增长

Yifei Lyu, Jun Nie, Shu-Kuei X. Yang
{"title":"利用跨国数据预测美国经济衰退中的增长","authors":"Yifei Lyu, Jun Nie, Shu-Kuei X. Yang","doi":"10.2139/ssrn.3690671","DOIUrl":null,"url":null,"abstract":"The Covid-19 pandemic has created tremendous downward pressure on economic activity and revived interest in forecasting economic growth during severe downturns However, most dynamic factor models used to forecast GDP growth include only domestic data We construct a large data set of 77 countries representing over 90 percent of global GDP and show that including cross-country data helps produce more accurate forecasts of US GDP growth during economic downturns, but is less helpful in normal times We provide explanations why this is the case","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"29 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Forecasting U.S. Economic Growth in Downturns Using Cross-Country Data\",\"authors\":\"Yifei Lyu, Jun Nie, Shu-Kuei X. Yang\",\"doi\":\"10.2139/ssrn.3690671\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Covid-19 pandemic has created tremendous downward pressure on economic activity and revived interest in forecasting economic growth during severe downturns However, most dynamic factor models used to forecast GDP growth include only domestic data We construct a large data set of 77 countries representing over 90 percent of global GDP and show that including cross-country data helps produce more accurate forecasts of US GDP growth during economic downturns, but is less helpful in normal times We provide explanations why this is the case\",\"PeriodicalId\":11495,\"journal\":{\"name\":\"Econometric Modeling: Capital Markets - Forecasting eJournal\",\"volume\":\"29 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Econometric Modeling: Capital Markets - Forecasting eJournal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3690671\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Econometric Modeling: Capital Markets - Forecasting eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3690671","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

然而,大多数用于预测GDP增长的动态因子模型只包括国内数据。我们构建了77个国家的大型数据集,占全球GDP的90%以上,结果表明,包括跨国数据有助于更准确地预测经济衰退期间的美国GDP增长。但在正常情况下用处不大。我们将解释为什么会出现这种情况
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Forecasting U.S. Economic Growth in Downturns Using Cross-Country Data
The Covid-19 pandemic has created tremendous downward pressure on economic activity and revived interest in forecasting economic growth during severe downturns However, most dynamic factor models used to forecast GDP growth include only domestic data We construct a large data set of 77 countries representing over 90 percent of global GDP and show that including cross-country data helps produce more accurate forecasts of US GDP growth during economic downturns, but is less helpful in normal times We provide explanations why this is the case
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