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Grace Juhn is a student at Harvard University’s Kennedy School of Government, and Prakash Loungani is Assistant to the Director of External Relations at the International Monetary Fund. Work on this paper was completed while Juhn was a Research Assistant in the IMF’s Research Department. We acknowledge useful comments from Frank Diebold.
Publications such as the IMF’s World Economic Outlook (WEO), the World Bank’s Global Economic Prospects (GEP), and the OECD’s Economic Outlook (EO) contain references to the Consensus forecasts. See, for instance, WEO: Interim Assessment (December 1997, pp. 34-36), Staff Studies for the WEO (December 1997, pp. 23-25), and GEP (1999, p. 9).
Earlier work by Loungani (2001a and b) also contains an evaluation of Consensus Forecasts of output growth. This paper builds on that work in five ways: (1) the entire sequence of bi-monthly forecasts is studied, instead of just the April and October forecasts; (2) forecast encompassing tests are presented to test more formally for the relative information content of private and official sector forecasts, instead of the scatter plots presented in the earlier work; (3) evidence is presented on directional accuracy of consensus and WEO forecasts; (4) the relationship between forecaster discord and forecast accuracy is studied; and (5) the sample period is extended by a year, not a trivial increase when the sample period is as short as it is here. The additional year is particularly useful in updating the evidence on forecasting recessions that was presented in the earlier work.
In future work, it would be interesting to examine the properties of the median forecast as well.
For example, the 1990 forecast was compared to the realization as reported in the M a y 1991 W E O. In cases where this was not possible, because the data were not reported, w e used the first available realization reported in the W E O
Preface to October 1998 WEO.
The correlation between Consensus Forecasts for any two adjacent months is very high, 0.95 or better. This suggests that our results are not likely to have been much affected by using the May forecasts instead of the April forecasts.
One interesting extension to pursue would be to see if forecast accuracy in the case of countries with IMF-supported programs differs from that in other cases. On the one hand, forecasts for program countries are subject to greater scrutiny, which may lead to greater accuracy. On the other hand, forecasts for program countries are often arrived at after negotiations with the country’s authorities and may not represent true forecasts. See Musso and Phillips (2002) for a further discussion and evidence on the accuracy of projections made as part of IMF-supported programs.
We carried out a test, based on Diebold and Mariano (1995) and Diebold (2001, pp. 293-94), of whether the better performance of the Consensus relative to the WEO is statistically significant. Our preliminary results suggest that it is, but this result will need to be tested more rigorously in future work. One reason is that the test is intended for a time series rather than a panel data context; we used fixed effects to control for the panel nature of our data, but this may not be an adequate control.
Equations of this kind can also be motivated on the basis of an older literature on combining forecasts (Bates and Granger (1969), and Granger and Ramanathan (1984)), where the focus is on finding the optimal linear combination of available forecasts of an event. Diebold (1989) discusses the links between the forecast combination and forecast encompassing literatures.
0See Gallo, Granger, and Jeon (2002) for evidence on copycat behavior among the individual forecasters included in the Consensus Forecasts.
That this jump coincides with the arrival of a new year suggests that there is a heightened focus by both forecasters and their clients in the growth outcomes for the current year and perhaps lesser interest in outcomes for the following year.
Cited with permission from Goldman Sachs.
In a related discussion, Loungani (2000, 2001a) discusses two classes of theories for why recessions might not be forecast. The first is that the information needed is lacking: forecasters either do not have access to reliable real-time information or lack reliable models for translating available information into predictions of a recession. The second is that the incentives for producing an "outlier" forecast (a recession or a strong boom) are lacking.
Recent work (e.g., Alizadeh, Brandt, and Diebold, 2002) makes it clear that the range of forecasts (that is, max.-min.) can be a very informative volatility measure. It would be interesting to use the range instead of the standard deviation in regressions of the sort reported in Table 8.
In principle, one could carry out a similar analysis for the year-ahead standard deviation as well.