Predictive Modelling of Health Expenditure in Italy: Using GARCH Techniques

Abstract

This study analyses Italy’s monthly data about health expenditure from January 2012 to October 2022, sourced from International Financial Statistics (IMF). Augmented Dickey-Fuller (ADF) tests confirm the series is non-stationary at levels, exhibiting random walk behaviour, but achieves stationarity after first differencing, indicating integration of order one [Et ~ I(1)]. Regression analysis reveals a significant 12-month lagged effect, where a 1% increase in prior health expenditure growth raises the current growth rate by 0.19%, reflecting annual seasonality (e.g., fiscal budgets, winter health costs). The constant term indicates a robust 4.26% monthly growth rate, driven by Italy’s aging population, rising medical costs, and universal healthcare system (SSN), consistent with 8–9% of GDP spending. ARIMA forecasting shows a 0.284% increase in current growth per 1% prior growth, while GARCH(1,1) modelling indicates a marginally significant 0.169% effect from 5-month lagged growth and persistent volatility from shocks like COVID-19. The small value of R2 and insignificant F-stat. value suggested unmodeled factors (e.g., GDP, inflation) drive variability. The 2012–2022 period, marked by economic recovery and the pandemic, underscores volatility, necessitating refined models and flexible budgeting for Italy’s healthcare system.