Bayesian Forecasting of Cohort Fertility

Abstract

What does statistics have to offer science and society, in this age of massive data, machine learning algorithms, and multiple online sources of tools for data analysis? I recall a few situations where statistics made a real difference and reinforced the impact of our discipline on society. Sometimes the difference lay in the insightful analysis and inference enabled by groundbreaking methods in our field like hypothesis testing, likelihood ratios, Bayesian models, jackknife, and bootstrap. But perhaps more often, the impacts came from thoughtful analyses before data were collected, and the questions that arose after the statistical analysis. The impact of understanding the problem, designing the experiment and data collections, conducting the pilot surveys, and raising important questions, is substantial. Through sensible explorations following formal statistical procedures, statisticians have made contributions in many domains. In this presentation, I recall some examples which made a long-lasting impact. Some of them, like randomization in clinical trials, known and familiar to all, are so ingrained in our practice that the role of statistics has been forgotten. Others may be less familiar but nonetheless benefited greatly from the critical input of statisticians. All remind us that our field remains today not only relevant but critical to science and society.

Childlessness and fertility by couples' educational gender (in)equality in Austria, Bulgaria, and France

“…methods have been used in forecasting (Schmertmann et al 2014) and modelling (Osiewalska 2015(Osiewalska , 2013. Bijak and Bryant (2016) recently carried out an overview of the use of Bayesian methods in demography.…”

Post-transitional regional fertility in Romania

“…In the same note, Fox, Klüsener, and Myrskylä (2015) point out that this causality hypothesis is not entirely supported, as the end of postponement and the economic outcome improvement can occur simultaneously. However, in line with the works of , Myrskylä, Goldstein, and Cheng (2013), and Schmertmann et al (2014), part of the fertility increase in highly developed countries is not attributed to this tempo effect but rather to a real fertility level increase.…”

A Flexible Bayesian Model for Estimating Subnational Mortality

“…Other examples can be found in the study of mortality, fertility, and migration (e.g., Alkema et al 2012; Bijak 2008; Congdon 2009; King and Soneji 2011; Sharrow et al 2013). Our approach has similarities to applications by authors in cause-of-death mortality estimation (Girosi and King 2008) and cohort fertility projection (Schmertmann et al 2014) but with a focus on addressing small-area estimation issues rather than forecasting.…”