Mortality Modelling and Forecasting: a Review of Methods

By H. Booth, L. Tickle. Annals of Actuarial Science, 2008

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Forecasting global stock market implied volatility indices (2018, methodology): “This is in addition to the preference for parsimony as an approach for avoiding over-parameterization when modelling data for forecasting (Booth and Tickle, 2008) and it is a recommended criterion for differentiating between forecasting models (Harvey, 1990).”
A Gravity Model of Mortality Rates for Two Related Populations (2011, methodology): “The parameters of the t process are estimated by MLE, and details of the algorithms are provided in”
Bayesian Forecasting of Cohort Fertility (2014, background): “There is a growing literature in forecasting mortality, much of it derived from Lee and Carter’s (1992) singular value decomposition approach for period mortality forecasts (e.g., Renshaw and Haberman 2006; Booth and Tickle 2008; Girosi and King 2008; Hyndman and Booth 2008).”
Forecasting: theory and practice (2020, methodology): “The introduction of stochastic time series models has the advantage of obtaining a forecast probability distribution rather than a deterministic point forecast and, also, enable the determination of forecast intervals (Booth and Tickle, 2008).”