Bayesian implementation of Rogers–Castro model migration schedules: An alternative technique for parameter estimation

Abstract

BACKGROUND The Rogers–Castro model migration schedule is a key model for migration trends over the life course. It is applied in a wide variety of settings by demographers to examine the relationship between age and migration intensity. This model is nonlinear and can have up to 13 parameters, which can make estimation difficult. Existing techniques for parameter estimation can lead to issues such as nonconvergence, sensitivity to initial values, or optimization algorithms that do not reach the global optimum. OBJECTIVE We propose a new method of estimating Rogers–Castro model migration schedule parameters that overcomes most common difficulties.