Disentangling Age, Cohort and Time Effects in the Additive Model

This paper presents a new approach to the old problem of linear dependency of age, cohort and time effects. It is shown that second differences of the effects can be estimated without any normalization restrictions, providing information on the shape of the age, cohort and time effect profiles, and enabling identification of structural breaks. A Wald test is provided to test the popular linear and quadratic specifications against a very general alternative. First differenced and level effects can then be consistently estimated with a small number of additional normalizing assumptions. Moreover, it is demonstrated that coefficients on additional exogenous regressors can be consistently estimated in this framework without the need for normalizing assumptions