文章基本信息

标题：Probabilistic projection of subnational total fertility rates
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作者：Hana Sevcikova ; Adrian E. Raftery ; Patrick Gerland 等
期刊名称：Demographic Research
印刷版ISSN：1435-9871
电子版ISSN：1435-9871
出版年度：2018
卷号：38
页码：1843-1884
DOI：10.4054/DemRes.2018.38.60
出版社：Max Planck Institute for Demographic Research
摘要：Background: We consider the problem of probabilistic projection of the total fertility rate (TFR) for subnational regions. Objective: We seek a method that is consistent with the UN’s recently adopted Bayesian method for probabilistic TFR projections for all countries and works well for all countries. Methods: We assess various possible methods using subnational TFR data for 47 countries. Results: We ﬁnd that the method that performs best in terms of out-of-sample predictive performance and also in terms of reproducing the within-country correlation in TFR is a method that scales each national trajectory from the national predictive posterior distribution by a region-speciﬁc scale factor that is allowed to vary slowly over time. Conclusions: Probabilistic projections of TFR for subnational units are best produced by scaling the national projection by a slowly time-varying region-speciﬁc scale factor. This supports the hypothesis of Watkins (1990, 1991) that within-country TFR converges over time in response to country-speciﬁc factors, and thus extends the Watkins hypothesis to the last 50 years and to a much wider range of countries around the world. Contribution: We have developed a new method for probabilistic projection of subnational TFR that works well and outperforms other methods. This also sheds light on the extent to which within-country TFR converges over time.
关键词：autoregressive model;Bayesian hierarchical model;correlation;scaling model;subnational projections;total fertility rate (TFR)