调查水平和随时间变化的多元回归估计

Pub Date : 2023-03-01 DOI:10.2478/jos-2023-0002
Anne Konrad, Y. Berger
{"title":"调查水平和随时间变化的多元回归估计","authors":"Anne Konrad, Y. Berger","doi":"10.2478/jos-2023-0002","DOIUrl":null,"url":null,"abstract":"Abstract Rotations are often used for panel surveys, where the observations remain in the sample for a predefined number of periods and then rotate out. The information of previous waves can be exploited to improve current estimates. We propose a multivariate regression estimator which captures all information available from both waves. By adding additional auxiliary variables describing the information of the rotational design, the proposed estimator captures the sample correlation between waves. It can be used for the estimation of levels and changes.","PeriodicalId":0,"journal":{"name":"","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Multivariate Regression Estimator of Levels and Change for Surveys Over Time\",\"authors\":\"Anne Konrad, Y. Berger\",\"doi\":\"10.2478/jos-2023-0002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Rotations are often used for panel surveys, where the observations remain in the sample for a predefined number of periods and then rotate out. The information of previous waves can be exploited to improve current estimates. We propose a multivariate regression estimator which captures all information available from both waves. By adding additional auxiliary variables describing the information of the rotational design, the proposed estimator captures the sample correlation between waves. It can be used for the estimation of levels and changes.\",\"PeriodicalId\":0,\"journal\":{\"name\":\"\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0,\"publicationDate\":\"2023-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.2478/jos-2023-0002\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.2478/jos-2023-0002","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

摘要旋转通常用于面板调查,在面板调查中,观察结果在样本中保留预定义的时间段,然后旋转出去。可以利用先前波的信息来改进当前的估计。我们提出了一种多元回归估计器,它可以捕获来自两个波的所有可用信息。通过添加描述旋转设计信息的附加辅助变量,所提出的估计器捕获了波之间的样本相关性。它可以用于估计水平和变化。
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
A Multivariate Regression Estimator of Levels and Change for Surveys Over Time
Abstract Rotations are often used for panel surveys, where the observations remain in the sample for a predefined number of periods and then rotate out. The information of previous waves can be exploited to improve current estimates. We propose a multivariate regression estimator which captures all information available from both waves. By adding additional auxiliary variables describing the information of the rotational design, the proposed estimator captures the sample correlation between waves. It can be used for the estimation of levels and changes.
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
×
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