  We develop a general framework for the identification of counterfactual
parameters in a class of nonlinear semiparametric panel models with fixed
effects and time effects. Our method applies to models for discrete outcomes
(e.g., two-way fixed effects binary choice) or continuous outcomes (e.g.,
censored regression), with discrete or continuous regressors. Our results do
not require parametric assumptions on the error terms or time-homogeneity on
the outcome equation. Our main results focus on static models, with a set of
results applying to models without any exogeneity conditions. We show that the
survival distribution of counterfactual outcomes is identified (point or
partial) in this class of models. This parameter is a building block for most
partial and marginal effects of interest in applied practice that are based on
the average structural function as defined by Blundell and Powell (2003, 2004).
To the best of our knowledge, ours are the first results on average partial and
marginal effects for binary choice and ordered choice models with two-way fixed
effects and non-logistic errors.
