RDP 2026-05: Real GDP Forecasts by International Organisations 1. Introduction

The paper examines the properties of real gross domestic product (GDP) forecasts by international organisations. These organisations commit substantial resources to their macroeconomic forecasts, with a forecast round taking multiple weeks of work by dozens of economists. This high amount of effort reflects the importance of the forecasts for the decisions of international organisations and others, and hence their importance for economic outcomes more broadly.

International organisations use their forecasts to inform key policy decisions. The International Monetary Fund (IMF) and World Bank draw on the published forecasts for an economy when deciding whether to make a loan or grant to that economy. The European Commission (EC) uses its growth forecasts when setting its annual budget. The IMF, World Bank, EC and Organisation for Economic Co-operation and Development (OECD) all rely on their growth forecasts when giving macroeconomic policy advice to national governments.

International organisation forecasts are also important due to their effects on external users. Central banks and fiscal authorities make their own growth forecasts to inform their policy decisions, but are also users of international organisation forecasts. A survey of national governments shows that a substantial majority agree that IMF forecasts ‘are valuable inputs to the economic policy process in my country’ (Genberg and Martinez 2014b, Figure 5). Other external users include financial market participants, with JP Morgan and Citigroup both reporting that clients pay attention to IMF forecasts (Genberg, Martinez and Salemi 2014). Since international organisation forecasts influence the expectations of both governments and the private sector, they affect investment and other decisions. Empirical work finds they have causal effects on economic outcomes. Afonso (2010) shows that EC growth forecasts influence bond yields, while Beaudry and Willems (2022) find that if the IMF makes overly optimistic growth forecasts, this causes an economic contraction later, which they attribute to governments and the private sector accumulating excessive debt in response to the optimistic forecast.

Forecasting is challenging, and best practice is to rigorously evaluate forecasts to identify opportunities for improvement. Both academic researchers and international organisations have investigated the properties of real GDP forecasts, as will be discussed in the literature review. Comparisons of accuracy hold forecasters accountable, and assist users in choosing how much attention to pay to different forecasters. It is also useful to test if the forecasts are as accurate as possible given the information available, a concept called ‘forecast rationality’ in the literature.^[1]

This paper makes three contributions to the literature. First, we compare the forecast accuracy for pairs of forecasters and test the rationality of forecasts using a large dataset of forecasts. The dataset comprises vintages of forecasts by four international organisations (the IMF, the World Bank, the EC and the OECD) and, for reference, the average forecast of respondents to the monthly survey by Consensus Economics. One strength of our dataset is that it covers as many economies as possible, allowing us to identify features of the forecasts that are general, rather than specific to particular groups of economies. Another strength is that it covers forecasts as recently as 2025, which makes the results relevant to current practice at international organisations. For all pairs of forecasters analysed, we find statistically significant differences in accuracy for only a few economies. In contrast, for each forecaster, we find statistically significant departures from forecast rationality for the majority of economies. The nature of this irrationality varies by economy, but often takes the form of optimistic bias, revisions that are too large, or forecasts that are too extreme.

Second, we extend the literature on comparing accuracy by deriving a new decomposition. We show that the difference in squared errors of a pair of forecasts can be decomposed into contributions from disagreement between the forecasts and the accuracy of the average forecast. Forecast accuracy tends to differ by more at longer horizons. Using our decomposition, we show that this occurs because of lower accuracy at longer horizons, and to a lesser extent, more disagreement at longer horizons.

Finally, we investigate two explanations for the rejection of rationality that may be especially relevant to international organisations. One explanation is that international organisations often make forecasts that are conditional on future outcomes of a set of explanatory variables. We derive conditions under which this results in the failure of rationality tests. Another explanation is that organisations may make ‘modal’ forecasts, which means they report the most likely outcome rather than the expected value of the outcome. Often the economy is expected to grow moderately, but there is a small chance of negative growth due to a financial crisis or other shock. The modal forecast is simply for moderate growth, while the expected value takes into account the chance of a crisis. We present empirical evidence suggesting that making modal forecasts could introduce a small optimistic bias in the forecasts, but the magnitude is small.

The remainder of this paper is organised as follows. Section 2 reviews the literature, while Section 3 describes our dataset. Section 4 compares the accuracy of the forecasters, including presenting our new decomposition of differences in forecast accuracy. The results of rationality tests are presented in Section 5. Section 6 investigates two explanations for the rejection of rationality tests. Finally, Section 7 concludes.

Footnote

In everyday speech, the term ‘rational’ is often used to refer to whether a person is reasonable. Forecast rationality in the technical sense is quite different. Some practices that are reasonable, such as the use of conditioning assumptions, can lead to rationality being rejected in statistical tests. See Section 2.2 for details on the definition. [1]