Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/284149 
Year of Publication: 
2024
Series/Report no.: 
cemmap working paper No. CWP01/24
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
Many structural econometric models include latent variables on whose probability distributions one may wish to place minimal restrictions. Leading examples in panel data models are individual-specific variables sometimes treated as "fixed effects" and, in dynamic models, initial conditions. This paper presents a generally applicable method for characterizing sharp identified sets when models place no restrictions on the probability distribution of certain latent variables and no restrictions on their covariation with other variables. In our analysis latent variables on which restrictions are undesirable are removed, leading to econometric analysis robust to misspecification of restrictions on their distributions which are commonplace in the applied panel data literature. Endogenous explanatory variables are easily accommodated. Examples of application to some static and dynamic binary, ordered and multiple discrete choice and censored panel data models are presented.
Subjects: 
Panel
Robust procedure
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

Files in This Item:
File
Size
782.29 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.