IDEAS home Printed from https://ideas.repec.org/p/sas/wpaper/20201.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: https://www.dss.uniroma1.it/RePec/sas/wpaper/20201_DIF.pdf
    File Function: First version, 2020
    Download Restriction: no
    ---><---

    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)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kung, Ko-Lun & MacMinn, Richard D. & Kuo, Weiyu & Tsai, Chenghsien Jason, 2022. "Multi-population mortality modeling: When the data is too much and not enough," Insurance: Mathematics and Economics, Elsevier, vol. 103(C), pages 41-55.
    2. Cadena, Meitner & Denuit, Michel, 2016. "Semi-parametric accelerated hazard relational models with applications to mortality projections," Insurance: Mathematics and Economics, Elsevier, vol. 68(C), pages 1-16.
    3. Claudio Morana, 2010. "Heteroskedastic Factor Vector Autoregressive Estimation of Persistent and Non Persistent Processes Subject to Structural Breaks," ICER Working Papers - Applied Mathematics Series 36-2010, ICER - International Centre for Economic Research.
    4. Booth, Heather, 2006. "Demographic forecasting: 1980 to 2005 in review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 547-581.
    5. Claudio Morana, 2014. "Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks," Working Papers 273, University of Milano-Bicocca, Department of Economics, revised May 2014.
    6. Rob Hyndman & Heather Booth & Farah Yasmeen, 2013. "Coherent Mortality Forecasting: The Product-Ratio Method With Functional Time Series Models," Demography, Springer;Population Association of America (PAA), vol. 50(1), pages 261-283, February.
    7. İshak Demi̇r & Burak A. Eroğlu & Seçi̇l Yildirim‐Karaman, 2022. "Heterogeneous Effects of Unconventional Monetary Policy on the Bond Yields across the Euro Area," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(5), pages 1425-1457, August.
    8. Niels Haldrup & Carsten P. T. Rosenskjold, 2019. "A Parametric Factor Model of the Term Structure of Mortality," Econometrics, MDPI, vol. 7(1), pages 1-22, March.
    9. Mario Forni & Luca Gambetti & Luca Sala, 2014. "No News in Business Cycles," Economic Journal, Royal Economic Society, vol. 124(581), pages 1168-1191, December.
    10. Rangan Gupta & Alain Kabundi & Stephen Miller & Josine Uwilingiye, 2014. "Using large data sets to forecast sectoral employment," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(2), pages 229-264, June.
    11. Yi‐Chiuan Wang & Jyh‐Lin Wu, 2015. "Fundamentals and Exchange Rate Prediction Revisited," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(8), pages 1651-1671, December.
    12. Mestekemper, Thomas & Windmann, Michael & Kauermann, Göran, 2010. "Functional hourly forecasting of water temperature," International Journal of Forecasting, Elsevier, vol. 26(4), pages 684-699, October.
    13. Chiara Casoli & Riccardo (Jack) Lucchetti, 2022. "Permanent-Transitory decomposition of cointegrated time series via dynamic factor models, with an application to commodity prices [Commodity-price comovement and global economic activity]," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 494-514.
    14. Li, Han & Hyndman, Rob J., 2021. "Assessing mortality inequality in the U.S.: What can be said about the future?," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 152-162.
    15. Mark J. Holmes & Arthur Grimes, 2005. "Is there long-run convergence of regional house prices in the UK?," Working Papers 05_11, Motu Economic and Public Policy Research.
    16. Matteo Barigozzi & Marc Hallin, 2023. "Dynamic Factor Models: a Genealogy," Papers 2310.17278, arXiv.org, revised Jan 2024.
    17. Reese, Simon, 2015. "Asymptotic Inference in the Lee-Carter Model for Modelling Mortality Rates," Working Papers 2015:16, Lund University, Department of Economics.
    18. Georges Bresson & Cheng Hsiao, 2011. "A functional connectivity approach for modeling cross-sectional dependence with an application to the estimation of hedonic housing prices in Paris," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 501-529, December.
    19. Yongfu Huang, 2011. "Private investment and financial development in a globalized world," Empirical Economics, Springer, vol. 41(1), pages 43-56, August.
    20. Morana, Claudio, 2007. "Multivariate modelling of long memory processes with common components," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 919-934, October.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sas:wpaper:20201. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Stefano Fachin (email available below). General contact details of provider: https://edirc.repec.org/data/ddrosit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.