Christian Bontemps , Jean-Pierre Florens , Nour Meddahi
{"title":"功能生态推断","authors":"Christian Bontemps , Jean-Pierre Florens , Nour Meddahi","doi":"10.1016/j.jeconom.2024.105918","DOIUrl":null,"url":null,"abstract":"<div><div>In this paper, we consider the problem of ecological inference when one observes the conditional distributions of <span><math><mrow><mi>Y</mi><mo>|</mo><mi>W</mi></mrow></math></span> and <span><math><mrow><mi>Z</mi><mo>|</mo><mi>W</mi></mrow></math></span> from aggregate data and attempts to infer the conditional distribution of <span><math><mrow><mi>Y</mi><mo>|</mo><mi>Z</mi></mrow></math></span> without observing <span><math><mi>Y</mi></math></span> and <span><math><mi>Z</mi></math></span> in the same sample. First, we show that this problem can be transformed into a linear equation involving operators for which, under suitable regularity assumptions, least squares solutions are available. We then propose the use of the least squares solution with the minimum Hilbert–Schmidt norm, which, in our context, can be structurally interpreted as the solution with minimum dependence between <span><math><mi>Y</mi></math></span> and <span><math><mi>Z</mi></math></span>. Interestingly, in the case where the conditioning variable <span><math><mi>W</mi></math></span> is discrete and belongs to a finite set, such as the labels of units/groups/cities, the solution of this minimal dependence has a closed form. In the more general case, we use a regularization scheme and show the convergence of our proposed estimator. A numerical evaluation of our procedure is proposed.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"248 ","pages":"Article 105918"},"PeriodicalIF":9.9000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Functional ecological inference\",\"authors\":\"Christian Bontemps , Jean-Pierre Florens , Nour Meddahi\",\"doi\":\"10.1016/j.jeconom.2024.105918\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>In this paper, we consider the problem of ecological inference when one observes the conditional distributions of <span><math><mrow><mi>Y</mi><mo>|</mo><mi>W</mi></mrow></math></span> and <span><math><mrow><mi>Z</mi><mo>|</mo><mi>W</mi></mrow></math></span> from aggregate data and attempts to infer the conditional distribution of <span><math><mrow><mi>Y</mi><mo>|</mo><mi>Z</mi></mrow></math></span> without observing <span><math><mi>Y</mi></math></span> and <span><math><mi>Z</mi></math></span> in the same sample. First, we show that this problem can be transformed into a linear equation involving operators for which, under suitable regularity assumptions, least squares solutions are available. We then propose the use of the least squares solution with the minimum Hilbert–Schmidt norm, which, in our context, can be structurally interpreted as the solution with minimum dependence between <span><math><mi>Y</mi></math></span> and <span><math><mi>Z</mi></math></span>. Interestingly, in the case where the conditioning variable <span><math><mi>W</mi></math></span> is discrete and belongs to a finite set, such as the labels of units/groups/cities, the solution of this minimal dependence has a closed form. In the more general case, we use a regularization scheme and show the convergence of our proposed estimator. A numerical evaluation of our procedure is proposed.</div></div>\",\"PeriodicalId\":15629,\"journal\":{\"name\":\"Journal of Econometrics\",\"volume\":\"248 \",\"pages\":\"Article 105918\"},\"PeriodicalIF\":9.9000,\"publicationDate\":\"2025-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Econometrics\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0304407624002690\",\"RegionNum\":3,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Econometrics","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0304407624002690","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
In this paper, we consider the problem of ecological inference when one observes the conditional distributions of and from aggregate data and attempts to infer the conditional distribution of without observing and in the same sample. First, we show that this problem can be transformed into a linear equation involving operators for which, under suitable regularity assumptions, least squares solutions are available. We then propose the use of the least squares solution with the minimum Hilbert–Schmidt norm, which, in our context, can be structurally interpreted as the solution with minimum dependence between and . Interestingly, in the case where the conditioning variable is discrete and belongs to a finite set, such as the labels of units/groups/cities, the solution of this minimal dependence has a closed form. In the more general case, we use a regularization scheme and show the convergence of our proposed estimator. A numerical evaluation of our procedure is proposed.
期刊介绍:
The Journal of Econometrics serves as an outlet for important, high quality, new research in both theoretical and applied econometrics. The scope of the Journal includes papers dealing with identification, estimation, testing, decision, and prediction issues encountered in economic research. Classical Bayesian statistics, and machine learning methods, are decidedly within the range of the Journal''s interests. The Annals of Econometrics is a supplement to the Journal of Econometrics.