  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.
