  A linear mixed-effects (LME) model is proposed for modelling and forecasting
single and multi-population age-specific death rates (ASDRs). The innovative
approach that we take in this study treats age, the interaction between gender
and age, their interactions with predictors, and cohort as fixed effects.
Furthermore, we incorporate additional random effects to account for variations
in the intercept, predictor coefficients, and cohort effects among different
age groups of females and males across various countries. In the
single-population case, we will see how the random effects of intercept and
slope change over different age groups. We will show that the LME model is
identifiable. Using simulating parameter uncertainty in the LME model, we will
calculate 95% uncertainty intervals for death rate forecasts. We will use data
from the Human Mortality Database (HMD) to illustrate the procedure. We assess
the predictive performance of the LME model in comparison to the Lee-Carter
(LC) models fitted to individual populations. Additionally, we evaluate the
predictive accuracy of the LME model relative to the Li-Lee (LL) model. Our
results indicate that the LME model provides a more precise representation of
observed mortality rates within the HMD, demonstrates robustness in calibration
rate selection, and exhibits superior performance when contrasted with the LC
and LL models.
