This paper introduces a new approach to econometric analysis of nonlinear panel data models when the number of observations per observational unit is small. In such models the presence of variables that are constant within, while varying across, units results in an incidental parameter problem. The approach taken in this paper removes these incidental parameters via projection, which produces a correspondence specifying all combinations of observed variables and within-unit-varying unobserved heterogeneity that are achievable by choice of some value of the unit-specific incidental parameters. With unit specific variables removed, there is no need for assumptions concerning their joint distribution with other variables. The result is an incomplete model which is typically partially identifying. Identified sets are characterized via moment inequalities using tools of random set theory. Examples of application to static and dynamic models with discrete or continuous outcomes using distribution free restrictions on within-unit-varying unobserved heterogeneity are presented.
Centre for microdata methods and practice