Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/79553
Authors: 
Schennach, Susanne
Year of Publication: 
2013
Series/Report no.: 
cemmap working paper, Centre for Microdata Methods and Practice CWP32/13
Abstract: 
This paper introduces a general method to convert a model defined by moment conditions involving both observed and unobserved variables into equivalent moment conditions involving only observable variables. This task can be accomplished without introducing infinite-dimensional nuisance parameters using a least-favourable entropy-maximising distribution. We demonstrate, through examples and simulations, that this approach covers a wide class of latent variables models, including some game-theoretic models and models with limited dependent variables, interval-valued data, errors-in-variables, or combinations thereof. Both point- and set-identified models are transparently covered. In the latter case, the method also complements the recent literature on generic set-inference methods by providing the moment conditions needed to construct a GMM-type objective function for a wide class of models. Extensions of the method that cover conditional moments, independence restrictions and some state-space models are also given.
Subjects: 
method of moments
latent variables
unobservables
partial identification
entropy
simulations
least-favorable family
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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