A Dynamic Factor-Copula Framework for Cause-Specific Mortality Forecasting and Dependent Multiple Decrements: Evidence from U.S. Cause-of-Death Rates, 1979-2023 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Dynamic Factor-Copula Framework for Cause-Specific Mortality Forecasting and Dependent Multiple Decrements: Evidence from U.S. Cause-of-Death Rates, 1979-2023 Anastasios-Tsampikos Statiou, Peter Hatzopoulos This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9290989/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper develops a dynamic factor-copula framework for coherent forecasting of all-cause and cause-specific mortality under a dependent multiple-decrement structure. The empirical application uses a U.S. cause-of-death panel covering 1979–2023, two sexes, 207 cause codes, and grouped ages from 0 to 100+. The model combines an all-cause Lee-Carter component, a cause-share factor layer estimated on logit-transformed decrement shares, and a Gaussian copula linking latent innovations across grouped causes. This architecture preserves aggregate coherence while allowing dependence across decrement-specific dynamics. After rescaling the source rates and regrouping causes into 13 macro-causes, forecasts were generated through 2070 using 500 stochastic trajectories. The results show numerically exact share and rate coherence, projected mortality declines for both sexes, and substantial long-horizon compositional change, with cardiovascular mortality losing relative weight while diabetes/metabolic, respiratory, and mental/neurological causes become more prominent. The copula layer reveals non-trivial latent cross-group dependence, especially involving respiratory mortality. Overall, the framework provides a tractable basis for coherent cause-specific forecasting, dependent decrement analysis, and future extensions toward richer dependence structures. mortality forecasting Lee-Carter cause-specific mortality multiple decrements Gaussian copula actuarial modeling Full Text Additional Declarations No competing interests reported. Supplementary Files USAe.csv dynamicfactorcopulamortalitymodelfixedv5paperholdout3d.r USAdlongidr.csv USAmlongidr.csv Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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