Population Forecasts for Bangladesh, Using a Bayesian Methodology
Mahsin, Md., Hossain, Syed Shahadat, Journal of Health Population and Nutrition
A widely-used method of forecasting the age- and sex-specific population for future years, in which the initial population is stratified by age and sex and projections, is generated by application of survival ratios and birth rates, followed by an additive adjustment for net migration. To get this information, the behaviour of the related variables is analyzed based on the past data by statisticians, and then inferences are drawn from the analysis to make forecasts of the desired variable. At present, there exist two major paradigms in statistics, namely conventional (frequentist) and Bayesian statistics for the purpose of data analysis. Use of Bayesian methodology in the field of data analysis is comparatively new and has found massive support in the last two decades from the experts belonging to various disciplines. Probably, the main reason behind the increasing support is its flexibility and generality that allows it to deal with the complex situations. Besides, Bayesian method is typically preferred over classical approach in parameter estimation because of the intractable form of the likelihood function (1).
There are a number of methodologies used for population projections. One of the most popular methods is cohort component method which is based on the estimates about the future levels of fertility, mortality, sex composition, migration, and other parameters. Many studies have examined the relative performance of simple mathematical models, extrapolation based on time-series and cohort-component models of population forecasting. Most have found that constant growth mathematical models or standard time-series models of population growth are as least accurate as cohor component models (2-4).
The present study is not intended to assess the relative accuracy of various projection models. Rather, it only aims to investigate the usefulness of cohort component method in making the population projection for Bangladesh, using Bayesian approach. Bayesian analysis has been applied in cohort component model for providing a neat and transparent way of estimation. It provides probabilistic point estimates of the parameters, along with the highest posterior density interval (HPD) or Bayesian credible interval. Bayesian credible interval is a measure of uncertainty, and it is based on statistical theory and data on error distributions that provide an explicit estimate of the probability that a given range will contain the future population. This approach develops statistical prediction intervals to accompany population forecasts (5-7). Prediction intervals will provide extremely valuable information to data-users and will improve the quality of decision-making, based on population forecasts.
A cohort component strategy of population projection is based on the logic of a general population-component methodology which examines separately the components of population change, fertility, mortality, and net migration. The cohort-component model of population projection (CCMPP) is perhaps the iconic method in demography (8-16). This classic method forwards, in time, a population defined by age according to a specified life table and set of age-specific fertility rates, taking into account the net migration at each age. A very basic equation can show the whole model:
where, t is the starting point of time; n is the projection interval; P(t) is the population-size at time t; and P(t+n) is the population size at time t+n. If we put immigrants and emigrants together, then
where, Net Migrants=Immigrant-Emigrants. A population grows through the addition of births and immigrants and declines through the subtraction of deaths and out-migrants.
The term 'fertility' refers to the ability of an individual to give a livebirth (or births). …