The traditional high-low-medium scenario approach to quantifying
uncertainty in population forecasts has been criticized as lacking
probabilistic meaning and consistency. This paper shows, under
certain assumptions, how appropriately calibrated scenarios can be
used to approximate the uncertainty intervals on future population
size and age structure obtained with fully stochastic
forecasts. As many forecasting organizations already produce
scenarios and because dealing with them is familiar territory,
the methods presented here offer an attractive intermediate
position between probabilistically inconsistent scenario analysis
and fully stochastic forecasts.
References
[1] Alho, J.M. & Spencer, B.D. (1991). A population forecast as a data base: implementing the stochastic propagation of error. Journal of Official Statistics, 7, 295-310. Abstract can also be found in the ISI/STMA publication
[2] Alho, J.M. (1992). The magnitude of error due to different vital processes in population forecasts. International Journal of Forecasting, 8, 301-314. Abstract can also be found in the ISI/STMA publication
[3] Cohen, J.E. (1986). Population forecasts and confidence intervals for Sweden: a comparison of model-based and empirical approaches. Demography, 23, 105-126.
[4] Congressional Budget Office (2001). Uncertainty in social security's long-term finances: a stochastic analysis. CBO Paper, December 2001, Congress of the United States.
[5] Keilman, N., Pham, D.Q. & Hetland, A. (2002). Why population forecasts should be probabilistic-illustrated by the case of {N}orway. Demographic Research, 6, 410-453, Article 15.
[6] Keyfitz, N. (1972). On future population. J. Amer. Statist. Assoc., 67(338), 347-363.
[7] Lee, R.D. (1993). Modeling and forecasting the time series of US. fertility: age distribution, range, and ultimate level. International Journal of Forecasting, 9, 187-202. Abstract can also be found in the ISI/STMA publication
[8] Lee, R.D. (1999). Probabilistic approaches to population forecasting. In Frontiers of Population Forecasting, Vol. 24 of Supplement to Population and Development Review, Eds. W. Lutz, J. Vaupel and D. Ahlburg, pp. 156-190. New York: Population Council.
[9] Lee, R.D. & Carter, L. (1992a). Modeling and forecasting the time series of US. mortality. J. Amer. Statist. Assoc., 87, 187-202. Abstract can also be found in the ISI/STMA publication
[10] Lee, R.D. & Carter, L.R. (1992b). Modeling and forecasting U.S. mortality: rejoinder. J. Amer. Statist. Assoc., 87(419), 674-675.
[11] Lee, R.D. & Tuljapurkar, S. (1994). Stochastic population forecasts for the United States: beyond high, medium, and low. J. Amer. Statist. Assoc., 89(428), 1175-1189. Abstract can also be found in the ISI/STMA publication
[12] Lee, R.D. & Tuljapurkar, S. (1998). Stochastic forecasts for social security. In Frontiers in the Economics of Aging, Ed. D. Wise, pp. 393-420. Chicago: University of Chicago Press.
[13] Lutz, W. & Scherbov, S. (1998). An expert-based framework for probabilistic national population projections: the example of Austria. European Journal of Population, 14, 1-17.
[14] Lutz, W., Sanderson, W. & Scherbov, S. (2001). The end of world population growth. Nature, 412, 543-545.
[15] National Research Council (2000). Beyond Six Billion: Forecasting the World's Population, Eds. J. Bongaarts and R.A. Bulatao. Washington, DC: National Academy Press.
[16] Rice, J.A. (1995). Mathematical Statistics and Data Analysis, second edn. Belmont, California: Duxbury.
[17] Stoto, M. (1983). The accuracy of population projections. J. Amer. Statist. Assoc., 78, 13-20.
[18] Tuljapurkar, S. (1992). Stochastic population forecasts and their uses. International Journal of Forecasting, 8, 385-391. Abstract can also be found in the ISI/STMA publication