RDP 2026-05: Real GDP Forecasts by International Organisations 7. Conclusion

This paper provides new evidence and theory concerning real GDP forecasts by international organisations. The paper begins by comparing the forecast accuracy of four international organisations and the average of Consensus forecasts, finding statistically significant differences for only a few economies. It presents a new decomposition of differences in accuracy into contributions of disagreement and the accuracy of the average forecast. This decomposition is then used to show that, on average across economies, differences in accuracy are larger at longer forecast horizons due to both greater disagreement and lower accuracy of the average forecast. We then turn to forecast rationality. For most forecasters, we reject the null of the rationality test for most economies. Often the irrationality takes the form of optimistic bias, overly large revisions or forecasts that are too extreme. Finally, we contribute to the literature that attempts to explain why rationality is often rejected in these tests. We focus on two explanations: the use of conditioning assumptions and the practice of making modal forecasts.

Our findings have important implications for the practices of international organisations. Firstly, we find that the international organisations are quite similar to one another in terms of accuracy and the ways in which they fail rationality tests. Secondly, our findings highlight the importance of forecasters being transparent, including about their conditioning assumptions and whether they see themselves as making modal forecasts. This will assist users in interpreting the forecasts. Knowing that a forecaster is making modal forecasts, for instance, will tell users that they should not view the forecasts as incorporating tail risks like financial crises. Transparency will also make it easier to choose among competing explanations for the failure of rationality tests, which will help organisations determine what actions to take in response.

The lessons of this paper can also be applied to other types of forecasters. For example, one can ask whether differences in the accuracy of different private-sector forecasters are larger at longer horizons, and if so, use our decomposition to assess if this reflects greater disagreement or lower accuracy. Or one can investigate the effects of making conditioning assumptions or modal forecasts at central banks or fiscal authorities.