Identification and Estimation in Two-Sided Matching Markets
Nikhil Agarwal and
No 1905, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University
We study estimation and non-parametric identification of preferences in two-sided matching markets using data from a single market with many agents. We consider a model in which preferences of each side of the market are vertical, utility is non-transferable and the observed matches are pairwise stable. We show that preferences are not identified with data on one-to-one matches but are non-parametrically identified when data from many-to-one matches are observed. The additional empirical content in many-to-one matches is illustrated by comparing two simulated objective functions, one that does and the other that does not use information available in many-to-one matching. We also prove consistency of a method of moments estimator for a parametric model under a data generating process in which the size of the matching market increases, but data only on one market is observed. Since matches in a single market are interdependent, our proof of consistency cannot rely on observations of independent matches. Finally, we present Monte Carlo studies of a simulation based estimator.
Keywords: Two-sided matching; Identification; Estimation (search for similar items in EconPapers)
JEL-codes: C13 C14 C78 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-gth and nep-net
Date: 2013-08, Revised 2014-02
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Persistent link: https://EconPapers.repec.org/RePEc:cwl:cwldpp:1905
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