Simulation and sensitivity analysis of the choice of open interval, the methods of open interval, the methods of estimating life expectancy, completeness and 6 in the SEG method of estimating mortality - Msemburi, Willliam (Centre for Actuarial Research (CARE), 2014)
Death distribution methods, particularly the Generalized Growth Balance (GGB) and the Synthetic Extinct Generations (SEG) methods, have been observed to lead to the most accurate estimates when estimating mortality [1]. The more general version of the SEG method corrects for differential coverage of censuses directly by adding a constant (6) to the age-specific growth rates such that the correction leads to a horizontal series of age specific estimates of completeness. This research attempts to obtain the best variation of this version of the SEG method from a range of choices for an open interval age as well as well as methods of estimating life expectancy. completeness and 6. This task is accomplished by starting with a base population with known mortality then applying random errors in completeness. age misstatement and net migration to it to generate numerous datasets consisting of simulated census counts and simulated vital registration deaths by age. Variations of the SEG method are then applied to the simulated datasets to correct for the underestimation of mortality caused by the data errors. The best variations are found by statistical analysis of the difference between the true mortality and the estimated mortality for each variation and dataset generated. Using the Coale and Demeny model life tables to estimate life expectancy. selecting the a that results in a minimum variance in the age specific estimates of completeness. estimating completeness using the median value of the age specific. estimates Of completeness for ages 15 and older and using the 85+ age group for the open interval is observed to be the variation of the SEG method that leads to the most accurate estimates of mortality.