Bontemps, ChristianIdRef, Florens, Jean-PierreIdRef and Meddahi, NourIdRef (2025) Functional ecological inference. Journal of Econometrics, vol. 248.

Full text not available from this repository.
Identification Number : 10.1016/j.jeconom.2024.105918

Abstract

In this paper, we consider the problem of ecological inference when one observes the conditional distributions of Y|W and Z|W from aggregate data and attempts to infer the conditional distribution of Y|Z without observing Y and Z 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 Y and Z. Interestingly, in the case where the conditioning variable W 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.

Item Type: Article
Language: English
Date: March 2025
Refereed: Yes
Place of Publication: Amsterdam
Uncontrolled Keywords: Ecological inference, Linear operator, Generalized inverse, Hilbert–Schmidt norm, Regularization
Subjects: B- ECONOMIE ET FINANCE
Divisions: TSE-R (Toulouse)
Site: UT1
Date Deposited: 03 Jul 2025 09:08
Last Modified: 03 Jul 2025 09:09
OAI Identifier: oai:tse-fr.eu:130642
URI: https://publications.ut-capitole.fr/id/eprint/50969
View Item