cemmap working paper CWP20/23
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.
Research Fellow University College London
Andrew is the Director of the ESRC Centre for Microdata Methods and Practice (cemmap) and Professor of Economics and Economic Measurement at UCL.
Research Fellow Duke University
Adam is a Research Fellow associated with the Cemmap at the IFS and UCL and an Associate Professor of Economics at Duke University.
PhD Scholar University College London
Working Paper details
- Institute for Fiscal Studies
A, Chesher and A, Rosen and Y, Zhang. (2023). Identification analysis in models with unrestricted latent variables: Fixed effects and initial conditions. CWP20/23. London: Institute for Fiscal Studies. Available at: https://ifs.org.uk/publications/identification-analysis-models-unrestricted-latent-variables-fixed-effects-and-initial (accessed: 8 December 2023).
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