Bayesian Forecasting of Cohort Fertility

Carl Schmertmann, Emilio Zagheni, Joshua R. Goldstein and Mikko Myrskylä

Journal of the American Statistical Association, 2014, vol. 109, issue 506, 500-513

Abstract: There are signs that fertility in rich countries may have stopped declining, but this depends critically on whether women currently in reproductive ages are postponing or reducing lifetime fertility. Analysis of average completed family sizes requires forecasts of remaining fertility for women born 1970-1995. We propose a Bayesian model for fertility that incorporates a priori information about patterns over age and time. We use a new dataset, the Human Fertility Database (HFD), to construct improper priors that give high weight to historically plausible rate surfaces. In the age dimension, cohort schedules should be well approximated by principal components of HFD schedules. In the time dimension, series should be smooth and approximately linear over short spans. We calibrate priors so that approximation residuals have theoretical distributions similar to historical HFD data. Our priors use quadratic penalties and imply a high-dimensional normal posterior distribution for each country's fertility surface. Forecasts for HFD cohorts currently aged 15-44 show consistent patterns. In the United States, Northern Europe, and Western Europe, slight rebounds in completed fertility are likely. In Central and Southern Europe, East Asia, and Brazil, there is little evidence for a rebound. Our methods could be applied to other forecasting and missing-data problems with only minor modifications.

Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (37)

Downloads: (external link)
http://hdl.handle.net/10.1080/01621459.2014.881738 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Bayesian forecasting of cohort fertility (2012)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlasa:v:109:y:2014:i:506:p:500-513

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UASA20

DOI: 10.1080/01621459.2014.881738

Access Statistics for this article

Journal of the American Statistical Association is currently edited by Xuming He, Jun Liu, Joseph Ibrahim and Alyson Wilson

More articles in Journal of the American Statistical Association from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().