Population Forecasts, Fiscal Policy, and Risk
AbstractThis paper describes how stochastic population forecasts are used to inform and analyze policies related to government spending on the elderly, mainly in the context of the industrialized nations. The paper first presents methods for making probabilistic forecasts of demographic rates, mortality, fertility, and immigration, and shows how these are combined to make stochastic forecasts of population number and composition, using forecasts of the U.S. population by way of illustration. Next, the paper discusses how demographic models and economic models can be combined into an integrated projection model of transfer systems such as social security. Finally, the paper shows how these integrated models describe various dimensions of policy-relevant risk, and discusses the nature and implications of risk in evaluating policy alternatives.
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Bibliographic InfoPaper provided by Levy Economics Institute, The in its series Economics Working Paper Archive with number wp_471.
Date of creation: Aug 2006
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Web page: http://www.levyinstitute.org
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-11-25 (All new papers)
- NEP-FOR-2006-11-25 (Forecasting)
- NEP-MAC-2006-11-25 (Macroeconomics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Ron Lesthaeghe & Paul Willems, 1999. "Is Low Fertility a Temporary Phenomenon in the European Union?," Population and Development Review, The Population Council, Inc., vol. 25(2), pages 211-228.
- Hyndman, Rob J. & Booth, Heather, 2008.
"Stochastic population forecasts using functional data models for mortality, fertility and migration,"
International Journal of Forecasting,
Elsevier, vol. 24(3), pages 323-342.
- Rob J Hyndman & Heather Booth, 2006. "Stochastic population forecasts using functional data models for mortality, fertility and migration," Monash Econometrics and Business Statistics Working Papers 14/06, Monash University, Department of Econometrics and Business Statistics.
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