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Forecasting mortality rates and life expectancy in the year of Covid-19

Author

Listed:
  • Francesca Di Iorio

    (University of Naples Federico II)

  • Stefano Fachin

    ("Sapienza" University of Rome)

Abstract

Forecasting mortality rates and life expectancy is an issue of critical importance made arguably more difficult by the effects the current Covid-19 pandemic. In this paper we compare the performances of a simple random walk model (benchmark), three variants of the standard Lee-Carter model (Lee-Carter, Lee-Miller, Booth-Maindonald-Smith), the Hyndman-Ullah functional data analysys model, and a general factor model. We use both symmetric and asymmetric loss functions, as the latter are arguably more suitable to capture preferences of forecast users such as insurance companies and pension and health system planners. In a counterfactual study, designed exploiting the particular features of Italian data, we reproduce the likely impact of Covid-19 on forecasts using 2020 as a jump-off year. To put the results in perspective, we also carry out out a general assessment on 1950-2016 data for three countries with very diverse demographic profiles, France, Italy and USA. While the results with these latter datasets suggest that in normal conditions the Lee-Miller and Hyndman-Ullah models are somehow superior,from the counterfactual study the best option appears to be the Booth-Maindonald- Smith model.

Suggested Citation

  • Francesca Di Iorio & Stefano Fachin, 2020. "Forecasting mortality rates and life expectancy in the year of Covid-19," DSS Empirical Economics and Econometrics Working Papers Series 2020/1, Centre for Empirical Economics and Econometrics, Department of Statistics, "Sapienza" University of Rome.
    • Handle: RePEc:sas:wpaper:20201
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    File URL: https://www.dss.uniroma1.it/RePec/sas/wpaper/20201_DIF.pdf
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    References listed on IDEAS

    as
    1. Declan French & Colin O'Hare, 2013. "A Dynamic Factor Approach to Mortality Modeling," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(7), pages 587-599, November.
    2. Cary Chi-Liang Tsai & Shuai Yang, 2015. "A Linear Regression Approach to Modeling Mortality Rates of Different Forms," North American Actuarial Journal, Taylor & Francis Journals, vol. 19(1), pages 1-23, January.
    3. Bai, Jushan, 2004. "Estimating cross-section common stochastic trends in nonstationary panel data," Journal of Econometrics, Elsevier, vol. 122(1), pages 137-183, September.
    4. Hyndman, Rob J. & Shahid Ullah, Md., 2007. "Robust forecasting of mortality and fertility rates: A functional data approach," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4942-4956, June.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Mortality forecasting; life expectancy forecasting; Lee-Carter; factor model; Covid-19.;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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