Volume 49 - Article 42 | Pages 1201–1228  

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

By Jessie Yeung, Monica Alexander, Tim Riffe

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.

Methods: We apply a Bayesian framework for fitting the Rogers–Castro model. We also provide the R package rcbayes with functions to easily apply our proposed methodology.

Results: We illustrate how this model and the R package can be used in a variety of settings by applying the model to data from the American Community Survey.

Contribution: We provide a novel and easy-to-use approach for estimating Rogers–Castro model parameters. Our approach is formalized in an R package that makes parameter estimation and Bayesian methods more accessible for demographers and other researchers.

Author's Affiliation

• Jessie Yeung - University of Toronto, Canada EMAIL
• Monica Alexander - University of Toronto, Canada EMAIL
• Tim Riffe - Euskal Herriko Unibertsitatea (University of the Basque Country), Spain EMAIL

Other articles by the same author/authors in Demographic Research

Developing and implementing the UN's probabilistic population projections as a milestone for Bayesian demography: An interview with Adrian Raftery
Volume 51 - Article 1

Measuring short-term mobility patterns in North America using Facebook advertising data, with an application to adjusting COVID-19 mortality rates
Volume 50 - Article 10

Editorial to the Special Issue on Demographic Data Visualization: Getting the point across – Reaching the potential of demographic data visualization
Volume 44 - Article 36

Lexis fields
Volume 42 - Article 24

Exploring the demographic history of populations with enhanced Lexis surfaces
Volume 42 - Article 6

Global estimation of neonatal mortality using a Bayesian hierarchical splines regression model
Volume 38 - Article 15

Symmetries between life lived and left in finite stationary populations
Volume 35 - Article 14

The force of mortality by life lived is the force of increment by life left in stationary populations
Volume 32 - Article 29