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3 - Empirically based specification of forecast uncertainty

Published online by Cambridge University Press:  22 September 2009

Juha M. Alho
Affiliation:
Professor of Statistics University of Joensuu, Finland
Harri Cruijsen
Affiliation:
Democast, The Netherlands
Nico Keilman
Affiliation:
University of Oslo, Norway
Juha M. Alho
Affiliation:
University of Joensuu, Finland
Svend E. Hougaard Jensen
Affiliation:
Odense Universitet, Denmark
Jukka Lassila
Affiliation:
Research Institute of the Finnish Economy
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Summary

Introduction

To make a conventional population forecast one needs to specify age-specific fertility rates for women, and mortality rates for women and men, for all future years of interest. These are used to generate births and deaths. The simplest way to handle migration is to specify net migration in absolute numbers that are added to population each year. Starting from a jump-off population, the so-called cohort-component bookkeeping (e.g. Shryock, Siegel and associates, 1976) is used recursively to keep track of the resulting changes in population, by age and sex. These methods were first used by Cannan (1895) for England and Wales, and since the 1920s and 1930s they have been widely used in Europe (DeGans, 1999). The early forecasters were aware that calculations based on the cohort-component method are only as reliable as the assumptions that go into making them. Alternative variants were offered from early on, but even the forecast producers themselves were uneasy about the methods that were used to prepare them (e.g. Modeen, 1934).

Stochastic (or probabilistic) cohort-component forecasts are similar, but in this case future fertility and mortality rates and net migration are considered as random variables (e.g. Alho and Spencer, 2005). Their distributions can be specified in various ways. Perhaps the simplest is to give first the location of the distribution, and then to specify the spread (or scale) around it to reflect forecast uncertainty.

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Publisher: Cambridge University Press
Print publication year: 2008

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References

Alho, J. M. (1990). ‘Stochastic Methods in Population Forecasting’. International Journal of Forecasting, 6: 521–30.CrossRefGoogle ScholarPubMed
Alho, J. M. (1998). A Stochastic Forecast of the Population of Finland. Review no. 1998/4. Helsinki: Statistics Finland.
Alho, J. M. (2005). ‘Simplified Approaches to Stochastic Multi-state Population Forecasts’. Paper presented at conference on Stochastic Demographic Forecasting, Salamanca, August.
Alho, J. M. and Spencer, B. D. (1997). ‘The Practical Specification of the Expected Error of Population Forecasts’. Journal of Official Statistics, 13: 203–25.Google Scholar
Alho, J. M. and Spencer, B. D. (2005). Statistical Demography and Forecasting. New York: Springer.Google Scholar
Auerbach, A. J. and Lee, R. D. (2001), eds. Demographic Change and Fiscal Policy. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, revised edn. San Francisco: Holden-Day.Google Scholar
Cannan, E. (1895). ‘The Probability of Cessation of the Growth of Population in England and Wales during the Next Century’. The Economic Journal, 5: 505–15.CrossRefGoogle Scholar
Chib, S. and Greenberg, E. (1994). ‘Bayes Inference in Regression Models with ARMA(p,q) Errors’. Journal of Econometrics, 64: 183–206.CrossRefGoogle Scholar
DeGans, H. A. (1999). Population Forecasting 1895–1945. Dordrecht: Kluwer.CrossRefGoogle Scholar
Freedman, D., Pisani, R. and Purves, R. (1978). Statistics. New York: Norton.Google Scholar
Eurostat, (2005). ‘EUROPOP2004, Population Projections: EU25 Population Rises Until 2025, Then Falls’. News Release 448/2005, 8 April.
Keilman, N. (1990). Uncertainty in National Population Forecasting: Issues, Backgrounds, Analyses, Recommendations. Amsterdam: Swets and Zeitlinger.Google Scholar
Keilman, N. (1997). ‘Ex-post Errors in Official Population Forecasts in Industrialized Countries’. Journal of Official Statistics, 13: 245–77.Google Scholar
Keilman, N. and Pham, D. Q. (2004). Empirical Errors and Predicted Errors in Fertility, Mortality and Migration Forecasts in the European Economic Area. Discussion Paper no. 386. Oslo: Statistics Norway.Google Scholar
Keilman, N., Pham, D. Q. and Hetland, A. (2002). Norway's Uncertain Demographic Future. Social and Economic Studies 105. Oslo: Statistics Norway.Google Scholar
Lee, R. D. (1974). ‘Forecasting Births in Post-transition Populations: Stochastic Renewal with Serially Correlated Fertility’. Journal of the American Statistical Association, 69: 607–17.CrossRefGoogle ScholarPubMed
Lee, R. D. and Carter, L. R. (1992) ‘Modeling and Forecasting the Time Series of U.S. Mortality’. Journal of the American Statistical Association, 87: 659–71.Google Scholar
Lee, R. D. and Tuljapurkar, S. (1998). ‘Uncertain Economic Futures and Social Security Finances’. American Economic Review, 88: 237–41.Google Scholar
Modeen, G. (1934). ‘The Future Development of the Population of Finland’. Kansantaloudellinen aikakauskirja, 6: 351–78. (In Finnish.)Google Scholar
National Research Council (2000). Beyond Six Billion: Forecasting the World's Population. Panel on Population Projections. Washington, DC: National Academy Press.
Rao, J. N. K. (2003). Small Area Estimation. New York: Wiley.CrossRefGoogle Scholar
Shryock, H. S., Siegel, J. S. and associates (1976). The Methods and Materials of Demography, condensed edn by Stockwell, E. G.. New York: Academic Press.Google Scholar
Stoto, M. (1983). ‘Accuracy of Population Projections’. Journal of the American Statistical Association, 78: 13–20.CrossRefGoogle Scholar
United Nations (2004). The World Population Prospects: The 2004 Revision of the Population Database. New York: United Nations.

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