In the previous chapters much attention was paid to estimation and testing strategies using panel data in a variety of models.1 In practical situations, however, a true panel data set may not always be available, while repeated cross sections are. For example, in the United Kingdom, no panel data are available on consumer expenditures or labour supply. Nevertheless, a random sample of the population is available each year in the Family Expenditure Surveys (F.E.S.). Recently, several authors have stressed the fact that panel data are not indispensible for the identification of many commonly estimated models and that the parameters of interest can often be identified (with or without some additional assumptions) from a single cross section or a series of cross sections (see, for example, Heckman and Robb [1985], Deaton [1985] and Moffitt [1991]). In this chapter we shall discuss the identification and estimation of panel data models from repeated cross sections. In particular, attention will be paid to linear models with fixed individual effects and tomodels containing lagged endogenous variables.