  This paper presents an application of Generalized Estimating Equations (GEE)
for analyzing age-specific death rates (ASDRs), constituting a longitudinal
dataset with repeated measurements over time. GEE models, known for their
robustness in handling correlated data, offer a reliable solution when
individual data records lack independence, thus violating the commonly assumed
independence and identically distributed (iid) condition in traditional models.
In the context of ASDRs, where correlations emerge among observations within
age groups, two distinct GEE models for single and multipopulation ASDRs are
introduced, providing robust estimates for regression parameters and their
variances. We explore correlation structures, encompassing independence, AR(1),
unstructured, and exchangeable structures, offering a comprehensive evaluation
of GEE model efficiency in both single and multipopulation ASDRs. We critically
examines the strengths and limitations of GEE models, shedding light on their
applicability for mortality forecasting. Through detailed model specifications
and empirical illustrations, the study contributes to an enhanced understanding
of the nuanced capabilities of GEE models in predicting mortality rates.
