TY  - JOUR
AU  - Zarnowitz,Victor
AU  - Braun,Phillip
TI  - Twenty-two Years of the NBER-ASA Quarterly Economic Outlook Surveys: Aspects and Comparisons of Forecasting Performance
JF  - National Bureau of Economic Research Working Paper Series
VL  - No. 3965
PY  - 1994
Y2  - February 1994
UR  - http://www.nber.org/papers/w3965
L1  - http://www.nber.org/papers/w3965.pdf
N1  - Author contact info:
Victor Zarnowitz
Conference Board
845 3rd Avenue
New York, NY 10022-6679
Tel: 212/339-0432
Fax: 212/836-9757
E-Mail: no email available

Phillip Braun
University of Chicago
E-Mail: phillip.braun@ChicagoBooth.edu
M1  - published as Victor Zarnowitz, Phillip Braun. "Twenty-two Years of the NBER-ASA Quarterly Economic Outlook Surveys: Aspects and Comparisons of Forecasting Performance," in James H. Stock and Mark W. Watson, editors, "Business Cycles, Indicators and Forecasting" University of Chicago Press (1993)

AB  - The National Bureau of Economic Research, in co-operation with the American Statistical Association, conducted a regular quarterly survey of professional macroeconomic forecasters for 22 years beginning in 1968. The survey produced a mass of information about characteristics and results of the forecasting process. Many studies have already used some of this material. but this is the first comprehensive examination of all of It, This report addresses several subjects and produces findings on each, as follows: (I) The distributions of error statistics across the forecasters: the dispersion among the individual predictions is often large and it typically increases with forecast horizon, as do the mean absolute (or squared) errors. (2) The role of the time-series properties of the target data: the more volatile the time series, the larger as a rule are the errors of the forecasts. (3) The role of revisions in "actual" data: forecast errors tend to be larger the greater the extent of the revisions. (4) Differences by subperiod: there is little evidence of an overall improvement or deterioration in forecasts between the 1970s and the 19805. (5) Combining the individual forecasts into group mean or "consensus" forecasts: this generally results in large gains in accuracy. (6) Comparisons with a well-known macroeconometric model: the group forecasts are more accurate for most but not all variables and spans. (7) Comparisons with state-of-the-art time series models: the group forecasts and at least half of the individual forecasts tend to outperform Bayesian vector autoregressive models in most (but not all) cases. The univariate ARIMA forecasts arc generally the weakest.
ER  -