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.