Confidence intervals for population projections based on Monte Carlo methods
This paper presents an approach of constructing confidence intervals by means of Monte Carlo simulation. This technique attempts to incorporate the uncertainty involved in projecting human populations by letting the fertility and net immigration rates vary as a random variable with a specific distribution. Since fertility and migration are by far the most volatile, and therefore, the most critical components to population forecasting, this technique has the potential of accounting for this uncertainty, if the subjective distributions are specified with enough care. Considering the results of the model for the U.S. in 2082, for example, it is shown that the population will number between 255 million and 355 million with a probability of 90 percent.