Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/284150 
Year of Publication: 
2024
Series/Report no.: 
cemmap working paper No. CWP02/24
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
This paper proposes an information-based inference method for partially identified parameters in incomplete models that is valid both when the model is correctly specified and when it is misspecified. Key features of the method are: (i) it is based on minimizing a suitably defined Kullback-Leibler information criterion that accounts for incompleteness of the model and delivers a non-empty pseudotrue set; (ii) it is computationally tractable; (iii) its implementation is the same for both correctly and incorrectly specified models; (iv) it exploits all information provided by variation in discrete and continuous covariates; (v) it relies on Rao's score statistic, which is shown to be asymptotically pivotal.
Subjects: 
Misspecification
Partial Identification
Rao's score statistic
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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