The Blue Chip Survey: Moving Beyond the Consensus

Transcription

1 The Useful Role of Forecast Surveys Sponsored by NABE ASSA Annual Meeting, January 7, 2005 The Blue Chip Survey: Moving Beyond the Consensus Kevin L. Kliesen, Economist Work in Progress Not to be quoted without the author s permission

2 Outline Motivation January 7, 2005 ASSA Annual Meeting 1

3 Motivation Outline of Today s Talk Forecasts go astray in the 1990s Could we have done better than the Consensus forecast? January 7, 2005 ASSA Annual Meeting 2

4 Motivation Outline of Today s Talk Forecast accuracy using real-time and current-vintage data. January 7, 2005 ASSA Annual Meeting 3

5 Motivation Outline of Today s Talk Forecast accuracy using real-time and currentvintage data. Constructing a weighted-average Blue Chip forecast. January 7, 2005 ASSA Annual Meeting 4

6 Motivation In the mid- to late-1990s, the consensus forecast began to regularly underpredict real GDP growth and overpredict inflation. January 7, 2005 ASSA Annual Meeting 5

7 Motivation In the mid- to late-1990s, many forecasters began to regularly underpredict real GDP growth and overpredict inflation. The failure of economic models based on history to anticipate the acceleration in productivity contributed to the recent persistent underprediction of economic growth and overprediction of inflation. Guiding policy by those models doubtless would have unduly inhibited what has been a remarkable run of economic prosperity. Chairman Greenspan, Monetary Policy and the Economic Outlook, June 17, January 7, 2005 ASSA Annual Meeting 6

8 Motivation Forecasts of Long-Term Labor Productivity Growth and Current- Real-Time Actual Estimates Percent SPF Real-Time Current Vintage January 7, 2005 ASSA Annual Meeting 7

9 Motivation 5 4 Real GDP Growth: 1-Year Ahead Forecasts and Actual Percent Percentage of Errors that are Positive (A-F): 64% Consensus Actual NOTE: Consensus forecasts from Dec. 10 issue of prior year. Percentage changes are Q4 to Q4. Actual observations are real-time estimates. January 7, 2005 ASSA Annual Meeting 8

10 Motivation 5 4 CPI Inflation: 1-Year Ahead Forecasts and Actual Percent Percentage of Errors that are Negative (A-F): 64% Consensus Actual NOTE: Consensus forecasts from Dec. 10 issue of previous year. Percentage changes are Q4 to Q4. Actual observations are real-time estimates. January 7, 2005 ASSA Annual Meeting 9

11 Motivation Question: Was there information in the Blue Chip report that might have given policymakers and economists some indication that the Consensus forecasts were going astray? January 7, 2005 ASSA Annual Meeting 10

12 Motivation January 7, 2005 ASSA Annual Meeting 11

13 Motivation What is the Blue Chip Survey? A monthly survey of approximately 50 private-sector forecasters. They are asked to forecast 15 economic variables on an annual basis and 12 variables on a quarterly basis. The consensus is the simple arithmetic mean of the approximately 50 forecasts. January 7, 2005 ASSA Annual Meeting 12

14 Data & Analysis The data set will use the Consensus, Top 10 Average and Bottom 10 Average forecasts for: Growth of Real GDP, CPI Inflation (All Items) and the unemployment rate (quarterly average of monthly values) Data set begins in July 1991 January 7, 2005 ASSA Annual Meeting 13

15 Data & Analysis Our data set will use the Consensus, Top 10 Average and Bottom 10 Average forecasts. I construct a data set that looks at quarterly forecasts using monthly updates. January 7, 2005 ASSA Annual Meeting 14

16 Data & Analysis Hence, I look at the progression of forecast errors in the following manner: January 7, 2005 ASSA Annual Meeting 15

17 Data & Analysis January 7, 2005 ASSA Annual Meeting 16

18 Data & Analysis January 7, 2005 ASSA Annual Meeting 17

19 Data & Analysis Finally, I construct a Weighted-Average 1- and 4-quarter ahead forecast using the Blue Chip Consensus, Top 10 and Bottom 10 average forecasts. January 7, 2005 ASSA Annual Meeting 18

20 A Weighted-Average Forecast Finally, I construct a Weighted Average 1- and 4- quarter ahead forecast using the Blue Chip Consensus, Top 10 and Bottom 10 average forecasts. I use the forecast combination methodology suggested by Diebold (2004), which regresses the actual value on the Consensus, Top 10, and Bottom 10 Forecasts: y t+h = β 0 + β 1 y C t+h,t + β 2 yt t+h,t + β 2 yb t+h,t January 7, 2005 ASSA Annual Meeting 19

21 A Weighted-Average Forecast Real GDP Growth Actual & Forecasts: 3 Months Ahead BC-Consensus Actual BC-Wtd Avg January 7, 2005 ASSA Annual Meeting 20

22 A Weighted-Average Forecast CPI Inflation Actual & Forecasts: 3 Months Ahead BC-Consensus Actual BC-Wtd Avg January 7, 2005 ASSA Annual Meeting 21

23 A Weighted-Average Forecast Forecast Accuracy: Consensus Vs. Weighted Avg. January 7, 2005 ASSA Annual Meeting 22

24 A Weighted-Average Forecast Forecasts for 2004:Q4: Consensus Vs. Weighted Avg. 4.4 Real GDP Growth 3 CPI Inflation Oct Nov Dec Oct Nov Dec Consensus Weighted Average Consensus Weighted Average January 7, 2005 ASSA Annual Meeting 23

25 Future Work: Incorporating Time Varying Weights Percent of Time Real GDP Growth Forecast Has Lowest Absolute Error Over an Eight Quarter Rolling Horizon Percent 100 Forecasts are for 1-Quarter Growth Rate, 3 Months Ahead Consensus Top 10 Bottom10 January 7, 2005 ASSA Annual Meeting 24

26 Conclusions There often is valuable information in the Top 10 and Bottom 10 average forecasts. Forecasts of inflation and unemployment rates are usually more accurate (lower RMSE) than those of real GDP growth. Use of real-time data matters more for series that are frequently revised, like real GDP. The payoff from constructing a weighted-average (combination) forecast is generally larger for real GDP growth than for inflation. January 7, 2005 ASSA Annual Meeting 25

27 QUESTIONS? January 7, 2005 ASSA Annual Meeting 26

28 Motivation Recent Literature Using Blue Chip Forecasts Schuh (2001): forecasts are largely unbiased but not efficient; no significant difference between real-time and current vintage data at annual frequency, particularly for inflation, unemployment rates and interest rates. Gavin and Mandel (2002): they compared Blue Chip forecasts of real GDP growth and CPI inflation to FOMC bi-annual forecasts; Blue Chip forecasts for real GDP growth are as accurate as FOMC forecasts, but FOMC does better at forecasting inflation. Krane (2003): looked at forecasts from ; large and persistent real GDP forecast errors from viewed as transitory by forecasters ( typical ), but they then raised their short- and long-run growth estimates around 2000 when economic growth began to wane. Eisenbeis, et. al (2003), McNees (1992, 1995), Romer & Romer (2000) January 7, 2005 ASSA Annual Meeting 14