TY - JOUR
AU - Lee,Ronald
AU - Miller,Timothy
AU - Anderson,Michael
TI - Stochastic Infinite Horizon Forecasts for Social Security and Related Studies
JF - National Bureau of Economic Research Working Paper Series
VL - No. 10917
PY - 2004
Y2 - November 2004
DO - 10.3386/w10917
UR - http://www.nber.org/papers/w10917
L1 - http://www.nber.org/papers/w10917.pdf
N1 - Author contact info:
Ronald Lee
Departments of Demography and Economics
University of California, Berkeley
2232 Piedmont Avenue
Berkeley, CA 94720
Tel: 510/642-4535
Fax: 510/643-8558
E-Mail: rlee@demog.berkeley.edu
Timothy Miller
E-Mail: tmiller@demog.berkeley.edu
Michael L. Anderson
Department of Agricultural and Resource Economics
207 Giannini Hall, MC 3310
University of California, Berkeley
Berkeley, CA 94720
Tel: 510/642-7628
Fax: 510/643-8911
E-Mail: mlanderson@berkeley.edu
AB - This paper consists of three reports on stochastic forecasting for Social Security, on infinite horizons, immigration, and structural time series models. 1) In our preferred stochastic immigration forecast, total net immigration drops from current levels down to about one million by 2020, then slowly rises to 1.2 million at the end of the century, with 95% probability bounds of 800,000 to 1.8 million at the century's end. Adding stochastic immigration makes little difference to the probability distribution of the old age dependency ratio. 2) We incorporate parameter uncertainty, stochastic trends, and uncertain ultimate levels in stochastic models of wage growth and fertility. These changes sometimes substantially affect the probability distributions of the individual input forecasts, but they make relatively little difference when embedded in the more fully stochastic Social Security projection. 3) Using a 500-year stochastic projection, we estimate an infinite horizon balance of -5.15% of payroll, compared to the -3.5% of the 2004 Trustees Report, probably reflecting different mortality projections. Our 95% probability interval bounds are -10.5 and -1.3%. Such forecasts, which reflect only "routine" uncertainty, have many problems but nonetheless seem worthwhile.
ER -