Identification and estimation of dynamic models with a time series of repeated cross-sections*

Repeated cross-sectional data contain information on independent cross-sections of individual units at two or more points in time. Estimation of dynamic models with such data is made difficult by the general lack of information on lagged dependent and independent variables and the consequent unobservability of the intertemporal covariances needed to identify and estimate dynamic models. It is demonstrated here that the parameters of such models, both linear and nonlinear, both with and without fixed individual effects, are identified and can be consistently estimated with the imposition of certain restrictions. The paper includes an examination of the identification and estimation with repeated cross-sectional data of dynamic discrete dependent variable models, which can be parameterized in terms of transition rates between the different cross-sections.