Forecasting demographic forecasts

Consider the financial sustainability of public finances in the context of stochastic demographics. Such analyses have typically been made under the assumption that future demographic developments are deterministic. When stochastic demographics have been considered, the problems have been simplified by assuming that the decision makers in the economic system behave as if they had perfect foresight as regards demographics. More realistically, we assume that the decision makers base their decisions on the forecasts of the future population, but revise their decisions when it turns out that the demographics do not follow the expected path. We contrast the nature of demographic uncertainty with that of financial markets, and argue that it is not realistic to assume that the revisions will occur according to the full rational expectations paradigm. Instead, the decision makers are assumed to revise according to the most recent point forecast. To implement this approach, we tailor standard nonparametric regression techniques to the task of computing the required future conditional expectations. Specifically, we assume that an approximation to the predictive distribution of the future population is available in terms of simulated population counts. The required conditional expectations are then obtained by averaging the future evolution of a set of sample paths that come from the neighborhood of a target path. This is formally equivalent to n-nearest neighbor kernel regression. The degree of smoothing can be chosen via cross-validation. An illustration based on a stochastic forecast of the population of Finland is given.