Predictive Modelling of Health Expenditure in Italy: Using GARCH Techniques

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.

Stationarity, ADF Test, ACF, PACF, Angel-Granger Cointegration, ARIMA, ARCH, GARCH. JEL Classification: H51, H52, H53, H75, I15, I150, I180

1. Barma, S. C. (July, 2025) Forecasting INR Exchange Rates Using ARIMA: A Comparative Study with Euro, Pound, and Singapore Dollar. Journal of Emerging Technologies and Innovative Research, Vol-12, I-7. [Google Scholar] [Crossref]

2. Chaabouni, S., & Abednnadher, C. (2013). Modelling and forecasting of Tunisia’s health expenditures using artificial neural network and ARDL models. International Journal of Medical Science of Public Health, 2, 495–503. [Google Scholar] [Crossref]

3. Di Matteo, L. (2010). The sustainability of public health expenditures: Evidence from the Canadian federation. European Journal of Health Economics, 11(6), 569–584. [Google Scholar] [Crossref]

4. Getzen, T. (2000). Forecasting health expenditures: Short, medium, and long term. Journal of Health Care Finance, 26, 56–72. [Google Scholar] [Crossref]

5. Getzen, T., & Poullier, J.-P. (1992). International health spending forecasts: Concepts and evaluation. Social Science and Medicine, 34, 1057–1068. [Google Scholar] [Crossref]

6. Hannan, E. J., & Quinn, B. G. (1979). The determination of the order of an autoregression. Journal of Royal Statistical Society, B14, 190–195. [Google Scholar] [Crossref]

7. John Bryant, Audrey Teasdale, Martin Tobias, Jit Cheung and Mhairi McHugh (2004), Population Ageing and Government Health Expenditures in New Zealand, 1951-2051 . New Zealand Treasury Working Paper, No. 04/14. www.econstor.eu. [Google Scholar] [Crossref]

8. Lee, R., & Miller, T. (2002). An approach to forecasting health expenditures, with application to the U.S. medicare system. Health Services Research, 37(5), 1365–1386. [Google Scholar] [Crossref]

9. Paraskevi Klazoglou and Nikolaos Dritsakis (2018), Modeling and Forecasting of US Health Expenditures Using ARIMA Models. https://www.researchgate.net/publication/324567808. [Google Scholar] [Crossref]

10. Peijun, C. (2016), “Predictive Analysis of Chinese Total Health Expenditure Base on ARIMA Model”, Medicine and Society, (03). [Google Scholar] [Crossref]

11. Yue Z., W. Shengnan and L. Yuan (2015), “Application of ARIMA Model on Predicting Monthly Hospital Admissions and Hospitalization Expenses for Respiratory Diseases”, Chinese Journal of Health Statistics, (02), 197-200. [Google Scholar] [Crossref]

12. Zhao, J. (2015). Forecasting health expenditure: Methods and applications to international databases. Working Paper, Centre for Health Economics and Policy Analysis. [Google Scholar] [Crossref]

13. Ulf-G. Gerdtham, Bengt Jönsson (2000), International Comparisons of Health Expenditure: Theory, Data and Econometric Analysis. Handbook of Health Economics. Volume 1, Part A, 2000, Pages 11-53. [Google Scholar] [Crossref]

• Impact of Foreign Direct Investment in India
• Issues Involved in Digitalisation Special Reference to Indian Tourism Growth
• Relationship Marketing and Customer Loyalty in the Fast-Moving Consumer Goods (FMCG) Industry in Nairobi County
• Financial Literacy or Financial Inclusion? Which is Which, What is What—To Achieve Uganda’s 10-Fold Economic Growth By 2040
• Harnessing Natural Gas for Economic Transformation: Overcoming the Regulatory and Infrastructural Bottlenecks in Nigeria