LASSO for Stochastic Frontier Models with Many Efficient Firms

IF 2.9 2区 数学 Q1 ECONOMICS Journal of Business & Economic Statistics Pub Date : 2022-08-08 DOI:10.1080/07350015.2022.2110881
W. Horrace, Hyunseok Jung, Yoonseok Lee
{"title":"LASSO for Stochastic Frontier Models with Many Efficient Firms","authors":"W. Horrace, Hyunseok Jung, Yoonseok Lee","doi":"10.1080/07350015.2022.2110881","DOIUrl":null,"url":null,"abstract":"Abstract We apply the adaptive LASSO to select a set of maximally efficient firms in the panel fixed-effect stochastic frontier model. The adaptively weighted L 1 penalty with sign restrictions allows simultaneous selection of a group of maximally efficient firms and estimation of firm-level inefficiency parameters with a faster rate of convergence than least squares dummy variable estimators. Our estimator possesses the oracle property. We propose a tuning parameter selection criterion and an efficient optimization algorithm based on coordinate descent. We apply the method to estimate a group of efficient police officers who are best at detecting contraband in motor vehicle stops (i.e., search efficiency) in Syracuse, NY.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"41 1","pages":"1132 - 1142"},"PeriodicalIF":2.9000,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Business & Economic Statistics","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1080/07350015.2022.2110881","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 3

Abstract

Abstract We apply the adaptive LASSO to select a set of maximally efficient firms in the panel fixed-effect stochastic frontier model. The adaptively weighted L 1 penalty with sign restrictions allows simultaneous selection of a group of maximally efficient firms and estimation of firm-level inefficiency parameters with a faster rate of convergence than least squares dummy variable estimators. Our estimator possesses the oracle property. We propose a tuning parameter selection criterion and an efficient optimization algorithm based on coordinate descent. We apply the method to estimate a group of efficient police officers who are best at detecting contraband in motor vehicle stops (i.e., search efficiency) in Syracuse, NY.
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
具有多个有效企业的随机前沿模型的LASSO
摘要我们应用自适应LASSO在面板固定效应随机前沿模型中选择一组最大有效企业。具有符号限制的自适应加权L1惩罚允许同时选择一组最大有效的企业,并以比最小二乘伪变量估计器更快的收敛速度估计企业级低效率参数。我们的估计量具有预言性质。提出了一种基于坐标下降的调谐参数选择准则和一种有效的优化算法。我们应用该方法来估计纽约州锡拉丘兹市一组最擅长在机动车停车场发现违禁品的高效警察(即搜索效率)。
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
Journal of Business & Economic Statistics
Journal of Business & Economic Statistics 数学-统计学与概率论
CiteScore
5.00
自引率
6.70%
发文量
98
审稿时长
>12 weeks
期刊介绍: The Journal of Business and Economic Statistics (JBES) publishes a range of articles, primarily applied statistical analyses of microeconomic, macroeconomic, forecasting, business, and finance related topics. More general papers in statistics, econometrics, computation, simulation, or graphics are also appropriate if they are immediately applicable to the journal''s general topics of interest. Articles published in JBES contain significant results, high-quality methodological content, excellent exposition, and usually include a substantive empirical application.
期刊最新文献
A Ridge-Regularized Jackknifed Anderson-Rubin Test. Efficient and Robust Estimation of the Generalized LATE Model Modeling and Forecasting Macroeconomic Downside Risk* Causal inference under outcome-based sampling with monotonicity assumptions Functional-Coefficient Quantile Regression for Panel Data with Latent Group Structure
×
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