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Activity Number: 492
Type: Contributed
Date/Time: Wednesday, August 12, 2015 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #315424
Title: Bayesian Probabilistic Projection of International Migration
Author(s): Jonathan Azose* and Adrian Raftery
Companies: University of Washington and University of Washington
Keywords: Autoregressive model ; Bayesian hierarchical model ; International migration ; Demography
Abstract:

We propose a method for obtaining joint probabilistic projections of net international migration for all countries, broken down by age and sex. Our technique is to fit a Bayesian hierarchical first-order autoregressive model to net migration rate data. Joint trajectories for all countries are constrained to satisfy the requirement of zero global net migration. We evaluate our model using out-of-sample validation and compare point projections to the projected migration rates from a persistence model similar to the method used in the United Nations' World Population Prospects, and also to a state of the art gravity model. Our technique is a simple one that nonetheless overcomes some of the usual difficulties of migration projection. First, we produce both point and interval estimates, providing a natural quantification of uncertainty. Second, since our model uses only demographic variables as inputs, we can make long-term projections without explosion in the degree of uncertainty. Lastly, our projected trajectories approximately replicate the observed frequency of countries switching between positive and negative net migration.


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