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Combinatorial Bootstrap Inference IN in Prtially Identified Incomplete Structural Models

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Author Info

  • Marc Henry

    (Department of Economics, Universit� de Montr�al)

  • Romuald M�ango

    (Department of Economics, Universit� de Montr�al)

  • Maurice Queyranne

    (Sauder School of Business)

Abstract

We 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 Info

Paper provided by CIRJE, Faculty of Economics, University of Tokyo in its series CIRJE F-Series with number CIRJE-F-837.

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Length: 37 pages
Date of creation: Jan 2012
Date of revision:
Handle: RePEc:tky:fseres:2012cf837

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Cited by:
  1. Magnac, Thierry, 2014. "Identification partielle: méthodes et conséquences pour les applications empiriques," TSE Working Papers 14-458, Toulouse School of Economics (TSE).
  2. Andrew Chesher & Adam Rosen, 2013. "Generalised instrumental variable models," CeMMAP working papers CWP43/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  3. Laffers, Lukas, 2013. "Identification in Models with Discrete Variables," Discussion Paper Series in Economics 1/2013, Department of Economics, Norwegian School of Economics.

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