通用模型到专用模型简史*

IF 1.5 3区 经济学 Q2 ECONOMICS Oxford Bulletin of Economics and Statistics Pub Date : 2023-11-06 DOI:10.1111/obes.12578
David F. Hendry
{"title":"通用模型到专用模型简史*","authors":"David F. Hendry","doi":"10.1111/obes.12578","DOIUrl":null,"url":null,"abstract":"<p>We review key stages in the development of general-to-specific modelling (<i>Gets</i>). Selecting a simplified model from a more general specification was initially implemented manually, then through computer programs to its present automated machine learning role to discover a viable empirical model. Throughout, <i>Gets</i> applications faced many criticisms, especially from accusations of ‘data mining’—no longer pejorative—with other criticisms based on misunderstandings of the methodology, all now rebutted. A prior theoretical formulation can be retained unaltered while searching over more variables than the available sample size from non-stationary data to select congruent, encompassing relations with invariant parameters on valid conditioning variables.</p>","PeriodicalId":54654,"journal":{"name":"Oxford Bulletin of Economics and Statistics","volume":"86 1","pages":"1-20"},"PeriodicalIF":1.5000,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/obes.12578","citationCount":"0","resultStr":"{\"title\":\"A Brief History of General-to-specific Modelling*\",\"authors\":\"David F. Hendry\",\"doi\":\"10.1111/obes.12578\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>We review key stages in the development of general-to-specific modelling (<i>Gets</i>). Selecting a simplified model from a more general specification was initially implemented manually, then through computer programs to its present automated machine learning role to discover a viable empirical model. Throughout, <i>Gets</i> applications faced many criticisms, especially from accusations of ‘data mining’—no longer pejorative—with other criticisms based on misunderstandings of the methodology, all now rebutted. A prior theoretical formulation can be retained unaltered while searching over more variables than the available sample size from non-stationary data to select congruent, encompassing relations with invariant parameters on valid conditioning variables.</p>\",\"PeriodicalId\":54654,\"journal\":{\"name\":\"Oxford Bulletin of Economics and Statistics\",\"volume\":\"86 1\",\"pages\":\"1-20\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2023-11-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1111/obes.12578\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Oxford Bulletin of Economics and Statistics\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/obes.12578\",\"RegionNum\":3,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Oxford Bulletin of Economics and Statistics","FirstCategoryId":"96","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/obes.12578","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0

摘要

我们回顾了从一般到特定建模(Gets)发展的关键阶段。从更一般的规范中选择简化模型最初是通过人工实现的,然后通过计算机程序到现在的自动化机器学习来发现可行的经验模型。在整个过程中,"Gets "的应用受到了许多批评,尤其是对 "数据挖掘 "的指责--这已不再是贬义词--以及其他基于对该方法误解的批评,现在都已被驳斥。在从非稳态数据中搜索比可用样本量更多的变量时,可以不改变先前的理论表述,从而在有效的条件变量上选择具有不变参数的一致、包含的关系。
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
A Brief History of General-to-specific Modelling*

We review key stages in the development of general-to-specific modelling (Gets). Selecting a simplified model from a more general specification was initially implemented manually, then through computer programs to its present automated machine learning role to discover a viable empirical model. Throughout, Gets applications faced many criticisms, especially from accusations of ‘data mining’—no longer pejorative—with other criticisms based on misunderstandings of the methodology, all now rebutted. A prior theoretical formulation can be retained unaltered while searching over more variables than the available sample size from non-stationary data to select congruent, encompassing relations with invariant parameters on valid conditioning variables.

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
Oxford Bulletin of Economics and Statistics
Oxford Bulletin of Economics and Statistics 管理科学-统计学与概率论
CiteScore
5.10
自引率
0.00%
发文量
54
审稿时长
>12 weeks
期刊介绍: Whilst the Oxford Bulletin of Economics and Statistics publishes papers in all areas of applied economics, emphasis is placed on the practical importance, theoretical interest and policy-relevance of their substantive results, as well as on the methodology and technical competence of the research. Contributions on the topical issues of economic policy and the testing of currently controversial economic theories are encouraged, as well as more empirical research on both developed and developing countries.
期刊最新文献
Issue Information The Real Effects of Zombie Lending in Europe Factoring in the Micro: A Transaction‐Level Dynamic Factor Approach to the Decomposition of Export Volatility The Growth Effect of State Capacity Revisited 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