Abstract
Population forecasters have paid too little attention to forecast accuracy, uncertainty, and approaches other than the cohort-component method. They should track forecast errors and use them to adjust forecasts. They have chosen measures of forecast accuracy arbitrarily, with the result that flawed error measures are widely used in population forecasting. An examination of past forecasts would help establish what approaches are most accurate in particular applications and under what circumstances. Researchers have found that alternative approaches to population forecasting, including econometric models and extrapolation, provide more accurate forecasts than the cohort-component method in at least some situations. If they can determine the conditions under which these approaches are best, they can use them instead of the established method or in combination with it.
Methodological advances have made it possible to produce population forecasts with a greater degree of disaggregation and decomposition than before. If this decomposition allows a better understanding of the causal forces underlying population change, then decomposition may improve forecast accuracy. Even if disaggregation and decomposition do not improve overall forecast accuracy, they may lead to improved understanding or accurate forecasts of important components of the population, such as the elderly widowed population. Uncertainty has not been well-integrated into population forecasts. Researchers are pushing ahead in three main areas: population forecasts that include probability distributions; combining expert judgment and statistical methods; and the specification of situations that provide an internally consistent forecast of the population under particular circumstances. Evidence suggests that relying on experts to choose the fertility and mortality assumptions of the forecast has done little to improve forecast accuracy, but this is probably because expert opinion has been obtained in an unstructured way. Experience in other areas of forecasting has shown how to use experts to improve forecast accuracy.
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References
Ahlburg, D. A. (1992), “Error measures and the choice of a forecast method,” International Journal of Forecasting, 8, 99–100.
Ahlburg, D. A. (1995), “Simple versus complex models: Evaluation, accuracy, and combining,” Mathematical Population Studies, 5, 281–290.
Ahlburg, D. A. (1998), “Using economic information and combining to improve forecast accuracy in demography,” Working paper, Industrial Relations Center, University of Minnesota, Minneapolis MN 55455.
Ahlburg, D. A. and K.C. Land (1992), “Population forecasting: Guest editors’ introduction,” International Journal of Forecasting, 8, 289–299.
Alho, J. (1990), “Stochastic methods in population forecasts,” International Journal of Forecasting, 6, 521–530.
Alho, J. (1997). “Scenarios, uncertainty, and conditional forecasts of the world population,” Journal of the Royal Statistical Society, Series A, Part 1,160: 71–85.
Alho, J. and B. D. Spencer (1985), “Uncertain population forecasting,” Journal of the American Statistical Association, 80, 306–314.
Alho, J. and B. D. Spencer, (1990), “Error models for official mortality forecasts,” Journal of the American Statistical Association, 85, 609–616.
Allen, P. G. and R. Fildes (2001). “Econometric forecasting,” in J.S. Armstrong, Ed., Principles of Forecasting. Norwell, MA. Kluwer Academic Publishers.
Arkes, H. (2001), “Overconfidence in judgmental forecasting,” in J. S. Armstrong (ed.), Principles of Forecasting. Norwell, MA. Kluwer Academic Publishers.
Armstrong, J. S. (1985), Long-Range Forecasting (2nd ed.). New York: John Wiley.
Armstrong, J. S. (2001a), “Combining forecasts,” in J. S. Armstrong (ed.), Principles of Forecasting. Norwell, MA: Kluwer Academic Publishers.
Armstrong, J. S. ( 2001 b), “Evaluating forecasting methods,” in J. S. Armstrong (ed.), Principles of Forecasting. Norwell, MA: Kluwer Academic Publishers.
Armstrong, J. S. and F. Collopy (1992), “Error measures for generalizing about forecasting methods: Empirical comparisons,” International Journal of Forecasting, 8, 69–80.
Armstrong, J. S. and F. Collopy (1993), “Causal forces: Structuring knowledge for timeseries extrapolation,” Journal of Forecasting, 12, 103–115.
Carman, E. (1895), “The probability of a cessation of the growth of population in England and Wales during the next century,” Economic Journal, 5, 505–615.
Carter, L. (2000) “Imparting structural instability to mortality forecasts: Testing for sensitive dependence on initial conditions with innovations,” Mathematical Population Studies, 8, 31–54.
Chatfield, C. (2001), “Prediction intervals for time series,” in J. S. Armstrong (ed.), Principles of Forecasting. Norwell, MA: Kluwer Academic Publishers.
Clemen, R.T. (1989), “Combining forecasts: A review and annotated bibliography,” International Journal of Forecasting, 5, 559–584.
Collopy, F. and J. S. Armstrong (1992), “Rule-based forecasting: Development and validation of an expert systems approach to combining time series extrapolation,” Management Science, 38, 1394–1414.
Collopy, F. and J. S. Armstrong (1994), “Decomposition of time series by causal forces: Using domain knowledge to extrapolate highway deaths,” Working paper at hops.wharton.upenn.edu/forecast
Collopy, F., M. Adya and J. S. Armstrong (2001), “Expert systems in forecasting,” in J. S. Armstrong (ed.), Principles of Forecasting. Norwell, MA: Kluwer Academic Publishers.
Fildes, R. (1992) “The evaluation of extrapolative forecasting methods,” International Journal of Forecasting, 8, 81–98.
Fischer, I. and N. Harvey (1999), “Combining forecasts: What information do judges need to outperform the simple average?” International Journal of Forecasting, 15, 227–246.
Inoue, S. and Y. C. Yu (1979), “United Nations’ new population projections and analysis of ex post facto errors.” Paper presented at the Annual Meeting of the Population Association of America, Philadelphia, April.
Isserman, A. (1977), “The accuracy of population projections for subcounty regions,” Journal of the American Institute of Planners, 43, 247–259.
Keilman, N. (1990), Uncertainty in National Population Forecasting: Issues, Backgrounds, Analyses, Recommendations. Amsterdam: Swets and Zeitlinger.
Keilman, N. (1997), `Ex-post errors in official population forecasts in industrialized countries,“ Journal of Official Statistics, 13, 245–277.
Keilman, N. (1998), “How accurate are the United Nations’ world population projections?” Population and Development Review, 24 (supplement), 15–41.
Keyfitz, N. (1981), “The limits of population forecasting,” Population and Development Review, 7, 579–593.
Keyfitz, N. (1982), “Can knowledge improve forecasts?” Population and Development Review, 8, 729–751.
Lee, R. D. (1974), “Forecasting births in a post transition population: Stochastic renewal with serially correlated errors,” Journal of the American Statistical Association, 69, 607–617.
Lee, R. D. (1998), “Probabilistic approaches to population forecasting,” Population and Development Review, 24 (supplement), 156–190.
Lee, R. D., L. Carter and S. Tuljapurkar (1995), “Disaggregation in population forecasting: Do we need it? And how to do it simply,” Mathematical Population Studies, 5, 217234.
Long, J. F. (1995), “Complexity, accuracy, and utility of official population projections,” Mathematical Population Studies, 5, 203–216.
Lutz, W. (1994), Population, Development, Environment: Understanding their Interactions in Mauritius. Berlin: Springer-Verlag.
Lutz, W. (1995), “Scenario analysis in population projection,” IIASA working paper 9557 Laxenburg, Austria.
Lutz, W., A. Goujon and G. Doblhammer-Reiter (1998), “Adding education to age and sex,” Population and Development Review, 24 (supplement), 42–58.
Lutz, W., W. Sanderson. and S. Scherbov (1998), “Expert-based probabilistic population projections,” Population and Development Review, 24 (supplement), 139–155.
Lutz, W., J. W. Vaupel and D. A. Ahlburg (1998). “Frontiers of population forecasting,” in Special Issue of Population and Development Review, 24.
MacGregor, D. G. (2001) “Decomposition for judgmental forecasting and estimation,” in J. S. Armstrong (ed.), Principles of Forecasting. Norwell, MA: Kluwer Academic Publishers.
McNown, R. and A. Rogers (1992), “Forecasting cause specific mortality using tune series methods,” International Journal of Forecasting, 8, 413–432.
McNown, R, A. Rogers and J. Little (1995), “Simplicity and complexity in extrapolative population forecasting models,” Mathematical Population Studies, 8, 235–259.
Murdock, S., R. Hamm, P. Voss, D. Fannin and B. Pecotte, (1991) “Evaluating small area population projections”, Journal of the American Planning Association, 57, 432–443.
Murdock, S., F. Leistritz, R.R. Hamm, S-S Hwang and B. Parpia (1984) “An assessment of the accuracy of regional economic-demographic projection models,” Demography, 21, 383–404.
Pant, P. N. and W. H. Starbuck (1990), “Innocents in the forest: Forecasting and research methods,” Journal of Management, 16, 433–460.
Pflaumer, P. (1988), “The accuracy of U.N. population projections,” Proceedings, Annual Meeting. American Statistical Association, New Orleans, August, Social Statistics Section.
Pflaumer, P. (1992), “Forecasting U.S. population totals with the Box-Jenkins approach,” International Journal of Forecasting, 8, 329–338.
Rogers, A. (1995), “Population projections: Simple versus complex models,” Mathematical Population Studies, Special Issue, 5, 1–15.
Rowe, G. and G. Wright (2001), “Expert opinion in forecasting: The role of the Delphi technique,” in J. S. Armstrong (ed.), Principles of Forecasting. Norwell, MA: Kluwer Academic Publishers.
Sanderson, W. C. (1998), “Knowledge can improve forecasts! A review of selected socioeconomic population projection models,” Population and Development Review, 24 (supplement), 88–117.
Schmitt, R. and A. Crosetti (1951), “Accuracy of the ratio method for forecasting city populations,” Land Economics, 27, 346–348.
Siegel, J. S. (1953), “Forecasting the population of small areas,” Land Economics, 29, 7288.
Smith, S. K. (1987), “Tests of forecast accuracy and bias for county population projections,” Journal of the American Statistical Association, 82, 991–1003.
Smith, S. K. (1997), “Further thoughts on simplicity and complexity in population projection models,” International Journal of Forecasting, 13, 557–565.
Smith, S. K. and M. Shahidullah, (1995), “Evaluating population projection errors for census tracts,” Journal of the American Statistical Association, 90, 64–71.
Smith, S. K. and T. Sincich (1988), “Stability over time in the distribution of population forecast errors,” Demography, 25, 461–474.
Smith, S. K. and T. Sincich (1990), “On the relationship between length of base period and population forecast errors,” Journal of the American Statistical Association, 85, 367–375.
Smith, S. K. and T. Sincich (1991), “An empirical analysis of the effect of length of the forecast horizon on population forecast errors,” Demography, 28, 261–274.
Smith, S. K. and T. Sincich (1992), “Evaluating the forecast accuracy and bias of alternative population projections for states,” International Journal of Forecasting, 8, 495–508.
Smith, S.K., J. Tayman and D. Swanson, (2001), State and Local Population Projections:Methodology and Analysis. Plenum Publishers.
Stoto, M. (1983), “The accuracy of population projections,” Journal of the American Statistical Association, 78, 13–20.
Tayman, J., Schaefer, E. and L. Carter (1998), “The role of population size in the determination and prediction of population forecast error: An evaluation using confidence intervals for subcounty areas,” Population Research and Policy Review, 17, 1–20.
Van Imhoff, E. and W. Post (1998), “Microsimulation methods for population projection,” Population, 10, 97–138.
Voss, P. R. and B. D. Kale (1985), “Refinements to small-area population projection models: Results of a test based on 128 Wisconsin communities,” Paper presented at the annual meeting of the Population Association of America, Boston.
White, H. (1954), “Empirical study of the accuracy of selected methods of projecting state populations,” Journal of the American Statistical Association, 49, 480–498.
Zeng Y., J. W. Vaupel and W. Zhenglian (1998), “Household projection using conventional demographic data,” Population and Development Review, 24 (supplement), 59–87.
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Ahlburg, D.A. (2001). Population Forecasting. In: Armstrong, J.S. (eds) Principles of Forecasting. International Series in Operations Research & Management Science, vol 30. Springer, Boston, MA. https://doi.org/10.1007/978-0-306-47630-3_25
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