Flexibly Modeling Shocks to Demographic and Health Indicators with Bayesian Shrinkage Priors

TL;DR

This paper introduces a flexible Bayesian method to model unpredictable shocks in demographic and health indicators, improving the accuracy and calibration of trend estimates and projections without prior assumptions about shock timing or shape.

Contribution

It develops a novel Bayesian shrinkage approach that models shocks in demographic data without requiring prior knowledge of their occurrence or form.

Findings

Including shocks improves uncertainty interval sharpness.

Method maintains empirical coverage and prediction accuracy.

Flexible modeling captures short-term shocks effectively.

Abstract

Demographic and health indicators may exhibit short or large short-term shocks; for example, armed conflicts, epidemics, or famines may cause shocks in period measures of life expectancy. Statistical models for estimating historical trends and generating future projections of these indicators for a large number of populations may be biased or not well probabilistically calibrated if they do not account for the presence of shocks. We propose a flexible method for modeling shocks when producing estimates and projections for multiple populations. The proposed approach makes no assumptions about the shape or duration of a shock, and requires no prior knowledge of when shocks may have occurred. Our approach is based on the modeling of shocks in level of the indicator of interest. We use Bayesian shrinkage priors such that shock terms are shrunk to zero unless the data suggest otherwise. The method is demonstrated in a model for male period life expectancy at birth. We use as a starting point an existing projection model and expand it by including the shock terms, modeled by the Bayesian shrinkage priors. Out-of-sample validation exercises find that including shocks in the model results in sharper uncertainty intervals without sacrificing empirical coverage or prediction error.