Multi-country clustering-based forecasting of healthy life expectancy

Susanna Levantesi (), Andrea Nigri (), Gabriella Piscopo () and Alessandro Spelta ()
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Susanna Levantesi: Sapienza University of Rome
Andrea Nigri: University of Foggia
Gabriella Piscopo: University of Naples Federico II
Alessandro Spelta: Pavia University

Quality & Quantity: International Journal of Methodology, 2023, vol. 57, issue 2, No 5, 189-215

Abstract: Abstract Healthy life expectancy (HLE) is an indicator that measures the number of years individuals at a given age are expected to live free of disease or disability. HLE forecasting is essential for planning the provision of health care to elderly populations and appropriately pricing Long Term Care insurance products. In this paper, we propose a methodology that simultaneously forecasts HLE for groups of countries and allows for investigating similarities in their HLE patterns. We firstly apply a functional data clustering to the multivariate time series of HLE at birth of different countries for the years 1990–2019 provided by the Global Burden of Disease Study. Three clusters are identified for both genders. Then, we carry out the HLE simultaneous forecasting of the populations within each cluster by a multivariate random walk with drift. Numerical results and the statistical significance of the parameters of the identified multivariate processes are shown. Demographic evidences on the different evolution of HLE between countries are commented.

Keywords: Healthy life expectancy; Forecasting; Functional data clustering (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11135-022-01611-6

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