医药知识生成的内生性:泊松前沿模型的无仪器联结方法

IF 1.2 4区 管理学 Q3 ECONOMICS Journal of Economics & Management Strategy Pub Date : 2022-06-27 DOI:10.1111/jems.12491
Rouven E. Haschka, Helmut Herwartz
{"title":"医药知识生成的内生性:泊松前沿模型的无仪器联结方法","authors":"Rouven E. Haschka,&nbsp;Helmut Herwartz","doi":"10.1111/jems.12491","DOIUrl":null,"url":null,"abstract":"<p>This study provides an assessment of the R&amp;D–patent relation of European pharmaceutical firms that are not flawed by endogeneity biases. Firms invest in R&amp;D and generate latent knowledge which then manifests in observable patent outcomes through a Poisson model. The process of turning R&amp;D into knowledge is described by a production process subject to inefficiency and endogeneity. To estimate a Poisson stochastic frontier model, the suggested novel copula-based approach directly accounts for the dependence between the endogenous regressors and the inefficiency component. Hence, its implementation does not require any instrumental variables. Simulation results underline that the proposed estimator outperforms conventional instrumental variable estimators. Neglecting endogeneity leads to a substantial underestimation of the R&amp;D elasticity of patents generated in the European pharmaceutical industry.</p>","PeriodicalId":47931,"journal":{"name":"Journal of Economics & Management Strategy","volume":null,"pages":null},"PeriodicalIF":1.2000,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jems.12491","citationCount":"3","resultStr":"{\"title\":\"Endogeneity in pharmaceutical knowledge generation: An instrument-free copula approach for Poisson frontier models\",\"authors\":\"Rouven E. Haschka,&nbsp;Helmut Herwartz\",\"doi\":\"10.1111/jems.12491\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This study provides an assessment of the R&amp;D–patent relation of European pharmaceutical firms that are not flawed by endogeneity biases. Firms invest in R&amp;D and generate latent knowledge which then manifests in observable patent outcomes through a Poisson model. The process of turning R&amp;D into knowledge is described by a production process subject to inefficiency and endogeneity. To estimate a Poisson stochastic frontier model, the suggested novel copula-based approach directly accounts for the dependence between the endogenous regressors and the inefficiency component. Hence, its implementation does not require any instrumental variables. Simulation results underline that the proposed estimator outperforms conventional instrumental variable estimators. Neglecting endogeneity leads to a substantial underestimation of the R&amp;D elasticity of patents generated in the European pharmaceutical industry.</p>\",\"PeriodicalId\":47931,\"journal\":{\"name\":\"Journal of Economics & Management Strategy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2022-06-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jems.12491\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Economics & Management Strategy\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/jems.12491\",\"RegionNum\":4,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Economics & Management Strategy","FirstCategoryId":"91","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/jems.12491","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 3

摘要

本研究对不存在内生性偏差的欧洲制药公司的研发与专利关系进行了评估。企业投资研发并产生潜在的知识,然后通过泊松模型表现为可观察的专利结果。将研发转化为知识的过程被描述为受制于低效率和内生性的生产过程。为了估计泊松随机前沿模型,本文提出了一种新的基于copula的方法,该方法直接考虑了内生回归量与无效率成分之间的依赖关系。因此,它的实现不需要任何工具变量。仿真结果表明,所提出的估计器优于传统的仪器变量估计器。忽视内生性导致了对欧洲制药行业专利的研发弹性的严重低估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

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
Endogeneity in pharmaceutical knowledge generation: An instrument-free copula approach for Poisson frontier models

This study provides an assessment of the R&D–patent relation of European pharmaceutical firms that are not flawed by endogeneity biases. Firms invest in R&D and generate latent knowledge which then manifests in observable patent outcomes through a Poisson model. The process of turning R&D into knowledge is described by a production process subject to inefficiency and endogeneity. To estimate a Poisson stochastic frontier model, the suggested novel copula-based approach directly accounts for the dependence between the endogenous regressors and the inefficiency component. Hence, its implementation does not require any instrumental variables. Simulation results underline that the proposed estimator outperforms conventional instrumental variable estimators. Neglecting endogeneity leads to a substantial underestimation of the R&D elasticity of patents generated in the European pharmaceutical industry.

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
CiteScore
3.20
自引率
5.30%
发文量
43
期刊最新文献
Vertical mergers without foreclosure Dynamic competition for customer memberships Forward contracting and the endogenous activity of heterogeneous firms On fraud and certification of green production Issue Information
×
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