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Generalization Of A Result On “Regressions, Short And Long”

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  • Molinari, Francesca
  • Peski, Marcin

Abstract

This paper is concerned with the problem of combining a data set that identifies the conditional distribution P(y|x) with one that identifies the conditional distribution P(z|x) to identify the regressions E(y|x,·) ≡ [E(y|x,z = j),j ∈ Z] when the conditional distribution P(y|x,z) is unknown. Cross and Manski (2002, Econometrica 70, 357–368) studied this problem and showed that the identification region of E(y|x,·) can be precisely calculated when y has finite support. Here we generalize the result of Cross and Manski, showing that the identification region can be precisely calculated also in the case in which y has infinite support.We are grateful to the co-editor Paolo Paruolo, an anonymous referee, Maria Goltsman, Nick Kiefer, Tymon Tatur, and Tim Vogelsang for useful comments. Any remaining errors are our own responsibility.Financial support from Northwestern University's Dissertation Year Fellowship is gratefully acknowledged.

Suggested Citation

  • Molinari, Francesca & Peski, Marcin, 2006. "Generalization Of A Result On “Regressions, Short And Long”," Econometric Theory, Cambridge University Press, vol. 22(1), pages 159-163, February.
  • Handle: RePEc:cup:etheor:v:22:y:2006:i:01:p:159-163_06
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    Cited by:

    1. Xavier D'Haultfoeuille & Christophe Gaillac & Arnaud Maurel, 2018. "Rationalizing Rational Expectations? Tests and Deviations," NBER Working Papers 25274, National Bureau of Economic Research, Inc.
    2. Romuald Meango & Marc Henry & Ismael Mourifie, 2025. "Combining stated and revealed preferences," Papers 2507.13552, arXiv.org, revised Nov 2025.
    3. D'Haultfoeuille, Xavier & Gaillac, Christophe & Maurel, Arnaud, 2024. "Linear Regressions with Combined Data," TSE Working Papers 24-1602, Toulouse School of Economics (TSE).
    4. Xavier D'Haultfoeuille & Christophe Gaillac & Arnaud Maurel, 2021. "Rationalizing rational expectations: Characterizations and tests," Quantitative Economics, Econometric Society, vol. 12(3), pages 817-842, July.
    5. Molinari, Francesca, 2020. "Microeconometrics with partial identification," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 355-486, Elsevier.
    6. Matthew A. Masten & Alexandre Poirier, 2018. "Identification of Treatment Effects Under Conditional Partial Independence," Econometrica, Econometric Society, vol. 86(1), pages 317-351, January.
    7. Francesca Molinari, 2019. "Econometrics with Partial Identification," CeMMAP working papers CWP25/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Sarah Moon, 2024. "Partial Identification of Individual-Level Parameters Using Aggregate Data in a Nonparametric Model," Papers 2403.07236, arXiv.org, revised Dec 2025.
    9. Matthew Masten & Alexandre Poirier, 2016. "Partial independence in nonseparable models," CeMMAP working papers 26/16, Institute for Fiscal Studies.
    10. David Pacini, 2012. "Least Square Linear Prediction with Two-Sample Data," Bristol Economics Discussion Papers 12/631, School of Economics, University of Bristol, UK.

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