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

Citation Type: Miscellaneous

Publication Year: 2023

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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 it 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 the 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 R package can be used in a variety of settings by applying it 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 which makes parameter estimation and Bayesian methods more accessible for demographers and other researchers.

Url: https://www.monicaalexander.com/pdf/dr_rcbayes.pdf

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Authors: Yeung, Jessie; Alexander, Monica; Riffe, Tim

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Data Collections: IPUMS USA

Topics: Methodology and Data Collection, Migration and Immigration

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