临近预测爱尔兰GDP

Antonello D’Agostino, K. McQuinn, D. O'Brien
{"title":"临近预测爱尔兰GDP","authors":"Antonello D’Agostino, K. McQuinn, D. O'Brien","doi":"10.1787/JBCMA-2012-5K92N2PWCCWB","DOIUrl":null,"url":null,"abstract":"This paper presents a dynamic factor model that produces nowcasts and backcasts of Irish quarterly GDP using timely data from a panel dataset of 35 indicators. We apply a recently developed methodology, whereby numerous potentially useful indicator series for Irish GDP can be availed of in a parsimonious manner and the unsynchronised nature of the release calendar for a wide range of higher frequency indicators can be handled. The nowcasts in this paper are generated by using dynamic factor analysis to extract common factors from the panel dataset. Bridge equations are then used to relate these factors to quarterly GDP estimates. We conduct an out-of-sample forecasting simulation exercise, where the results of the nowcasting exercise are compared with those of a standard benchmark model.","PeriodicalId":313514,"journal":{"name":"Oecd Journal: Journal of Business Cycle Measurement and Analysis","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"36","resultStr":"{\"title\":\"Nowcasting Irish GDP\",\"authors\":\"Antonello D’Agostino, K. McQuinn, D. O'Brien\",\"doi\":\"10.1787/JBCMA-2012-5K92N2PWCCWB\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a dynamic factor model that produces nowcasts and backcasts of Irish quarterly GDP using timely data from a panel dataset of 35 indicators. We apply a recently developed methodology, whereby numerous potentially useful indicator series for Irish GDP can be availed of in a parsimonious manner and the unsynchronised nature of the release calendar for a wide range of higher frequency indicators can be handled. The nowcasts in this paper are generated by using dynamic factor analysis to extract common factors from the panel dataset. Bridge equations are then used to relate these factors to quarterly GDP estimates. We conduct an out-of-sample forecasting simulation exercise, where the results of the nowcasting exercise are compared with those of a standard benchmark model.\",\"PeriodicalId\":313514,\"journal\":{\"name\":\"Oecd Journal: Journal of Business Cycle Measurement and Analysis\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-05-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"36\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Oecd Journal: Journal of Business Cycle Measurement and Analysis\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1787/JBCMA-2012-5K92N2PWCCWB\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Oecd Journal: Journal of Business Cycle Measurement and Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1787/JBCMA-2012-5K92N2PWCCWB","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 36

摘要

本文提出了一个动态因素模型,该模型使用来自35个指标的面板数据集的及时数据产生爱尔兰季度GDP的即时预测和反向预测。我们采用最近开发的方法,可以以节俭的方式利用爱尔兰GDP的许多潜在有用指标系列,并且可以处理各种更高频率指标的发布日历的不同步性质。本文采用动态因子分析方法从面板数据集中提取公共因子,生成临近预测。然后使用桥式方程将这些因素与季度GDP估计联系起来。我们进行样本外预测模拟练习,将临近预报练习的结果与标准基准模型的结果进行比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Nowcasting Irish GDP
This paper presents a dynamic factor model that produces nowcasts and backcasts of Irish quarterly GDP using timely data from a panel dataset of 35 indicators. We apply a recently developed methodology, whereby numerous potentially useful indicator series for Irish GDP can be availed of in a parsimonious manner and the unsynchronised nature of the release calendar for a wide range of higher frequency indicators can be handled. The nowcasts in this paper are generated by using dynamic factor analysis to extract common factors from the panel dataset. Bridge equations are then used to relate these factors to quarterly GDP estimates. We conduct an out-of-sample forecasting simulation exercise, where the results of the nowcasting exercise are compared with those of a standard benchmark model.
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
期刊最新文献
Reverse-engineering the business cycle with Petri nets A comparison of economic indicator analysis and Markov switching methods concerning the cycle phase dynamics: report Frequency based co-movement of inflation in selected euro area countries The Swedish business cycle, 1969-2013 Construction of composite business cycle indicators in a scarce data environment: A case study for Abu Dhabi
×
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