Identification analysis in models with unrestricted latent variables: Fixed effects and initial conditions

Andrew Chesher, Adam Rosen and Yuanqi Zhang

No 20/23, CeMMAP working papers from Institute for Fiscal Studies

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. Endogenous explanatory variables can be easily accommodated. Examples of application to some static and dynamic binary, ordered and multiple discrete choice panel data models are presented.

Date: 2023-10-11
New Economics Papers: this item is included in nep-dcm and nep-ecm
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Working Paper: Identification analysis in models with unrestricted latent variables: Fixed effects and initial conditions (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:azt:cemmap:20/23

DOI: 10.47004/wp.cem.2023.2023

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