Multi-population models to handle mortality crises in forecasting mortality: A case study from Hungary

Central and Eastern European countries faced a serious mortality crisis in the second part of the 20th century, resulting in many years of decreasing life expectancy. In the last few decades, however, this was followed by a period in which mortality improved. This dichotomy of past trends makes it difficult to forecast mortality by way of stochastic models that incorporate these countries' long-term historical data. The product-ratio model (Hyndman et al., 2013) is a model of the coherent type, which relies more closely on subpopulations with common socioeconomic backgrounds and perspectives to forecast mortality for all populations. This paper examines whether the product-ratio model is suitable for forecasting mortality in countries that have experienced serious mortality crises. To that end, we present a case study centered on Hungary, where the mortality crisis lasted three decades. The evaluation is founded on a comprehensive comparison of the product-ratio model and the classical Lee-Carter model. Our main finding is that in the Hungarian case, the product-ratio model is more reliably accurate than the classical Lee-Carter model. The superior performance of the product-ratio model may indicate that coherent models are better suited to handling mortality crises in forecasting mortality than are independent models.