Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/64747
Authors: 
Beresteanu, Arie
Molchanov, Ilya
Molinari, Francesca
Year of Publication: 
2010
Series/Report no.: 
cemmap working paper CWP40/10
Abstract: 
This paper illustrates how the use of random set theory can benefit partial identification analysis. We revisit the origins of Manski's work in partial identification (e.g., Manski (1989, 1990)), focusing our discussion on identification of probability distributions and conditional expectations in the presence of selectively observed data, statistical independence and mean independence assumptions, and shape restrictions. We show that the use of the Choquet capacity functional and of the Aumann expectation of a properly defined random set can simplify and extend previous results in the literature. We pay special attention to explaining how the relevant random set needs to be constructed, depending on the econometric framework at hand. We also discuss limitations in the applicability of specific tools of random set theory to partial identification analysis.
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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