Combinatorial Bootstrap Inference IN in Prtially Identified Incomplete Structural Models
AbstractWe propose a computationally feasible inference method infinite games of complete information. Galichon and Henry (2011) and Beresteanu, Molchanov, and Molinari (2011) show that such models are equivalent to a collection of moment inequalities that increases exponentially with the number of discrete outcomes. We propose an equivalent characterization based on classical combinatorial optimization methods that alleviates this computational burden and allows the construction of confidence regions with an effcient combinatorial bootstrap procedure that runs in linear computing time. The method can also be applied to the empirical analysis of cooperative and noncooperative games, instrumental variable models of discrete choice and revealed preference analysis. We propose an application to the determinants of long term elderly care choices.
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Bibliographic InfoPaper provided by CIRJE, Faculty of Economics, University of Tokyo in its series CIRJE F-Series with number CIRJE-F-837.
Length: 37 pages
Date of creation: Jan 2012
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- NEP-ALL-2012-02-01 (All new papers)
- NEP-DCM-2012-02-01 (Discrete Choice Models)
- NEP-ECM-2012-02-01 (Econometrics)
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- Magnac, Thierry, 2014. "Identification partielle: méthodes et conséquences pour les applications empiriques," IDEI Working Papers 814, Institut d'Économie Industrielle (IDEI), Toulouse.
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- Andrew Chesher & Adam Rosen, 2014. "Generalized instrumental variable models," CeMMAP working papers CWP04/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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