Probabilistic Estimation and Projection of the Annual Total Fertility Rate Accounting for Past Uncertainty: A Major Update of the bayesTFR R Package

Probabilistic Estimation and Projection of the Annual Total Fertility Rate Accounting for Past Uncertainty: A Major Update of the bayesTFR R Package

bayesTFR, autoregressive model, Bayesian hierarchical model, Markov chain Monte
Carlo, R, United Nations, world population prospects, annual projections, past TFR uncertainty

The bayesTFR package for R provides a set of functions to produce probabilistic projections of the total fertility rates for all countries, and is widely used, including as part
of the basis for the United Nations official population projections for all countries. Liu
and Raftery (2020) extended the theoretical model by adding a layer that accounts for
the past total fertility rate estimation uncertainty. A major update of bayesTFR implements the new extension. Moreover, a new feature of producing annual total fertility rate
estimation and projections extends the existing functionality of estimating and projecting
for five-year time periods. An additional autoregressive component has been developed in
order to account for the larger autocorrelation in the annual version of the model. This
article summarizes the updated model, describes the basic steps to generate probabilistic
estimation and projections under different settings, compares performance, and provides
instructions on how to summarize, visualize and diagnose the model results.

Peiran Liu

https://www.jstatsoft.org/article/view/v106i08

University of Washington

March 2023

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