Partially Linear Models under Data Combination

Xavier D'Haultfoeuille, Christophe Gaillac and Arnaud Maurel

No 29953, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: We consider the identification of and inference on a partially linear model, when the outcome of interest and some of the covariates are observed in two different datasets that cannot be linked. This type of data combination problem arises very frequently in empirical microeconomics. Using recent tools from optimal transport theory, we derive a constructive characterization of the sharp identified set. We then build on this result and develop a novel inference method that exploits the specific geometric properties of the identified set. Our method exhibits good performances in finite samples, while remaining very tractable. Finally, we apply our methodology to study intergenerational income mobility over the period 1850-1930 in the United States. Our method allows to relax the exclusion restrictions used in earlier work while delivering confidence regions that are informative.

JEL-codes: C14 C21 J62 (search for similar items in EconPapers)
Date: 2022-04
New Economics Papers: this item is included in nep-ecm and nep-lab
Note: LS PE TWP
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.nber.org/papers/w29953.pdf (application/pdf)

Related works:
Journal Article: Partially Linear Models under Data Combination (2025)
Working Paper: Partially Linear Models under Data Combination (2023)
Working Paper: Partially Linear Models under Data Combination (2022)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nbr:nberwo:29953

Ordering information: This working paper can be ordered from
http://www.nber.org/papers/w29953

Access Statistics for this paper

More papers in NBER Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC.
Bibliographic data for series maintained by ().