Description: Navigate a perspective on the process of developing estimates and projections of mortality rates, based on a Living to 100 paper by the teacher. Discover the approach and considerations an actuary (or others) can take to develop a mortality rate assumption for five time periods. It will describe techniques to estimate historical mortality rates from which to project future mortality rates.

Description: This paper compares two mortality projection models – the latest version of the CMI Mortality Projections Model and the latest version of MIM-2021.Both models were applied to data for England & Wales and the US, and the results were compared.

Description: "Living to 100 in the time of COVID-19: A Study of Late –Life Mortality Trajectories, Leonid A. Gavrilov, Ph.D., Natalia S. Gavrilova, Ph.D., Academic Research Centers, NORC at the University of Chicago Swedish Mortality Does Not Decelerate in Recent Cohorts, or Does it? – Roland Rau, Ph. D., Professor, University of Rostock & Max Planck Institute for Demographic Research, Rostock, Germany; Marcus Ebeling, Ph.D., Karin Modig, Ph.D., Associate Professor, Anders Ahlbom, Ph.D., Professor Emeritus, Karolinska Institute, Stockholm Sweden; Kai Kaufhold, Aktuar DAV, Ad Res Advanced Reinsurance Services GmbH, Cologne, Germany; Annika Ziegler, Lukas Stake, Hochschule Koblenz & Ad Res, Germany"

Description: "Establishing Methods for Annual Updates to Cohort Life Expectancies – R. Dale Hall, FSA, CERA, MAAAManaging Director of Research, Society of Actuaries Research Institute Longevity Disparity across Communities in the United States – Lijia Guo, Ph.D., ASA, MAAA , US Government Accountability Office, Applied Research and Methods Division, Washington DC"

Description: This session will discuss: • What is social insurance and advantages of financing LTSS that way • Design decision points for an LTSS program • Existing examples of programs in the US and Abroad • Challenges and differences in pricing state-based LTSS financial reform.

Description: This paper compares two mortality projection models – the latest version of the CMI Mortality Projections Model and the latest version of MIM-2021.Both models were applied to data for England & Wales and the US, and the results were compared.

Description: Discover an introduction to decomposing differences in life expectancy by age and cause of death. Take a look into demographic techniques that decompose life expectancies at birth and older ages into the portions contributed by later age groups and causes of death-. These have been in existence for some time and provide a convenient device for analyzing mortality change over time as well as differences between sexes, countries, and ethnic groups. The methods will be described and illustrated by spreadsheet examples.

Description: This paper and subsequent Railroad Retirement Board remarriage tables are results from a culmination of remarriage studies from the period 1947-56. From Transactions of Society of Actuaries 1960, Vol. 12, No. 32.

Description: "How long is long in longevity? – Jesús-Adrian Álvarez, Ph.D. - Danish Labour Market Supplementary Pension Fund (ATP), The Rate of Aging in humans is constant. New evidence – Marcus Ebeling, PhD., Research Group Mathematical and Actuarial Demography, Max Planck Institute for Demographic Research, Rostock, Germany, Department of Sociology and Demography, University of Rostock, Rostock, Germany; Roland Rau, Ph.D., Professor, Research Group Mathematical and Actuarial Demography, Max Planck Institute for Demographic Research, Rostock, Germany, C6 Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Håkan Malmström, Ph.D., C6 Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden; Anders Ahlbom, Ph.D., Professor Emeritus, Karen Modig, Ph.D., Associate Professor, C6 Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden Human Lifetimes: Finite But Not Necessarily Bounded – Fei Huang, PhD., School of Risk & Actuarial Studies, UNSW Sydney; Ross Maller, Ph.D., Emeritus Professor, Xu Ning, Ph.D., Research School of Finance, Actuarial Studies & Statistics, Australian National University, Brandon Milholland, Ph.D., IQVIA Plymouth Meeting, PA."

Description: Mortality Model Selection and Combination in RAlternative approaches for selecting mortality models and forecasting mortality have been proposed. The usual practice is to base mortality forecasts on a single mortality model selected using in-sample goodness-of-fit measures. However, cross-validation and other alternative out-of-sample measures are increasingly being used in model selection, and model combination methods are becoming a common alternative to using a single mortality model. Such methods combine predictions from multiple mortality models to reduce model choice uncertainty and improve forecast accuracy. Explore techniques to select and combine mortality models using the freely available open source R package CoMoMo (https://github.com/kessysalvatory/CoMoMo), which extends the widely used R package StMoMo (https://CRAN.R-project.org/package=StMoMo). We will use data for the USA and other countries to discuss and demonstrate four different model combination approaches including simple model averaging, Bayesian model averaging, model confidence set, and stacked regression ensembles.