Random Scenario Forecasts versus Stochastic Forecasts

30 Pages Posted: 14 Feb 2008

See all articles by Shripad Tuljapurkar

Shripad Tuljapurkar

Stanford University

Ronald D. Lee

University of California, Berkeley - Department of Demography; National Bureau of Economic Research (NBER)

Qi Li

Texas A&M University - Department of Economics

Date Written: January 1, 2004

Abstract

Probabilistic population forecasts are useful because they describe uncertainty in a quantitatively useful way. One approach (that we call LT) uses historical data to estimate stochastic models (e.g., a time series model) of vital rates, and then makes forecasts. Another (we call it RS) began as a kind of randomized scenario: we consider its simplest variant, in which expert opinion is used to make probability distributions for terminal vital rates, and smooth trajectories are followed over time. We use analysis and C:\Eudora\attach\demo_3_25_04.pdf examples to show several key differences between these methods: serial correlations in the forecast are much smaller in LT; the variance in LT models of vital rates (especially fertility) is much higher than in RS models that are based on official expert scenarios; trajectories in LT are much more irregular than in RS; probability intervals in LT tend to widen faster over forecast time. Newer versions of RS have been developed that reduce or eliminate some of these differences.

Suggested Citation

Tuljapurkar, Shripad and Lee, Ronald D. and Li, Qi, Random Scenario Forecasts versus Stochastic Forecasts (January 1, 2004). Michigan Retirement Research Center Research Paper No. WP 2004-073, Available at SSRN: https://ssrn.com/abstract=1092879 or http://dx.doi.org/10.2139/ssrn.1092879

Shripad Tuljapurkar (Contact Author)

Stanford University ( email )

Stanford, CA 94305
United States

Ronald D. Lee

University of California, Berkeley - Department of Demography ( email )

2232 Piedmont Avenue
Berkeley, CA 94720-2120
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Qi Li

Texas A&M University - Department of Economics ( email )

5201 University Blvd.
College Station, TX 77843-4228
United States
979-845-7349 (Phone)