Literature Review | RDP 2026-05: Real GDP Forecasts by International Organisations

RDP 2026-05: Real GDP Forecasts by International Organisations 2. Literature Review

Martin McCarthy, Umberto Collodel and Prakash Loungani

September 2026

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2.1 Comparing accuracy across forecasters

A number of papers have compared the accuracy of real GDP growth forecasts by different international organisations. Some papers compare measures of forecast performance, but do not formally test for differences in accuracy. For example, Hong and Tan (2014) find that the United Nations growth forecasts performed slightly better than those by the IMF and World Bank over 2000 to 2012. Fioramanti et al (2016) find that EC growth forecasts were more accurate than market consensus and performed comparably to those of other international organisations over 2000 to 2014. Other papers formally test for accuracy. Timmermann (2007) finds that there are few economies where IMF and Consensus had statistically significant differences in accuracy. Melander, Sismanidis and Grenouilleau (2007) also find few economies where the IMF, OECD, Consensus or EC had significant differences in performance, using a sample of European economies from 1990 to 2005. Abreu (2011) uses a sample of nine advanced economies over 1991 to 2009, and finds few statistically significant differences in accuracy when comparing international organisations (IMF, EC, OECD) to private-sector forecasters (Consensus or The Economist). More recently, Akgun et al (2024) compare the accuracy of IMF and OECD forecasts from 1998 to 2016, finding few economies for which the forecasts differed in accuracy.

The lack of statistically significant differences in accuracy in the previous literature may partly reflect the small sample sizes used in many of those studies. This argument was made, for instance, by Timmermann (2007). Our paper includes more recent forecasts than earlier studies, increasing our sample size. Another explanation for the lack of significant differences is that the forecasts themselves tend to be quite similar. This similarity could arise because the forecasters independently make similar forecasts to one another due to having similar information sets. It could also arise if forecasters deliberately adjust their forecasts to be closer to others. The empirical evidence on whether forecasters deliberately ‘herd’ by making their forecasts more similar to one another is mixed (Clements 2018).

2.2 Forecast rationality

The literature describes a number of related notions of rationality. We follow Elliott, Komunjer and Timmermann (2008) in defining a forecaster to be ‘rational’ if their forecasts minimise the conditional expectation of the forecaster's loss function given their information set. To formalise this, let:

$y_{t + h}$ be realised real GDP growth in period $t + h$
$y_{t + h}_{| t}$ be a forecast made in period t for real GDP growth in period $t + h$
$v_{t + h}_{| t} \equiv y_{t + h} - y_{t + h}_{| t}$ be the forecast error, which is the actual less the forecast.
$L : ℝ \to ℝ_{\geq 0}$ be the forecaster's loss function
I_t is the forecaster's information set at period t

The forecaster is rational if:

y_{t + h}_{| t} = \underset{z}{\arg \min E} [L (y_{t + h} - z) | I_{t}] \forall t

Here, the conditional expectation is computed using the conditional probabilities implied by the true data-generating process (DGP). This is quite a stringent definition, as it assumes that the forecaster has a correctly specified model. Any mis-specification, such as the forecaster viewing growth as stationary when it is instead subject to breaks in the mean, would violate forecast rationality.

The literature often focuses on the special case where the forecaster's loss function is the squared error, $L (v_{t + h}_{| t}) = v_{t + h | t}^{2}$ . Jointly assuming forecast rationality and a squared error loss function is equivalent to assuming that the forecast equals the conditional expectation of real GDP growth given the forecaster's information set. This is called ‘strong efficiency’ by Nordhaus (1987):

y_{t + h}_{| t} = E [y_{t + h} | I_{t}] \forall t

If the forecaster's forecasts equal the conditional expectation of growth given their information set, then the forecast errors must be orthogonal to any vector x_t of variables known at time t. This orthogonality condition is called ‘strong rationality’ by Stekler (2004) and is the basis for the regression-based rationality tests common in the literature.^[2] If the forecast error satisfies Equation (1), where $u_{t + h}$ is a mean-zero error, then orthogonality states that the parameter vector $θ$ is the zero vector:

(1)

v_{t + h | t} = x_{t} θ + u_{t + h} \forall_{t}

The literature on growth forecasts contains various regressions of forecast errors on information available at the time of the forecast. Much of this literature tests the rationality of the average of private-sector forecasters (Coibion and Gorodnichenko 2012, 2015) or individual private-sector forecasters (Bordalo et al 2020; Broer and Kohlhas 2024). However, a number of papers test the rationality of international organisations' forecasts for multiple economies, as we do.^[3] These papers include rationality regressions with a variety of explanatory variables, including:

Regressions of forecast errors on an intercept to test for bias. The results are mixed. For the IMF, some papers have found an optimistic bias for many economies (Artis 1996; Timmermann 2007), while others have found optimistic biases for some economies and pessimistic biases for others (Schavey and Beach 1999; Takagi and Kucur 2006). For the OECD, Vogel (2007) finds that current-year forecasts are unbiased, while year-ahead forecasts have an optimistic bias. Tsuchiya (2023) finds that the same is true of World Bank forecasts since the global financial crisis. Finally, for the EC, Melander et al (2007) finds no evidence of bias for all but a few economies.
Regressions of forecast errors on past revisions to test if the forecaster tends to revise forecasts by too much or too little. Ashiya (2006) performs this test on IMF and OECD forecasts for G7 economies, rejecting rationality in 40 per cent of cases, typically because the revisions are too small. In contrast, Aktuğ and Rezghi (2025) find that IMF real GDP growth forecasts tend to be overrevised.
Regressions of forecast errors on an intercept and forecast. This can be used to test if the forecasts are ‘too extreme’ in the sense that they should be scaled up or scaled down in magnitude. Equivalently, one can regress actual outcomes on an intercept and forecast, as in Mincer and Zarnowitz (1969).^[4] Tsuchiya (2023) finds that this test is rejected for one-year-ahead forecasts by the World Bank, while Vogel (2007) finds the same for the OECD.
Regressions of forecast errors on errors known at the time. If forecast errors are positively correlated with past forecast errors known to the forecaster, then the forecaster is failing to learn from their mistakes. Timmermann (2007) finds that IMF forecast errors have positive sample autocorrelations, but do not formally test the null of no autocorrelation. Melander et al (2007) do not reject the null of rationality for EC forecasts for most economies.
Regressions of real GDP growth forecast errors on actual outcomes or forecasts for other explanatory variables. For example, Ghirelli, Pérez and Santabárbara (2025) show that IMF and European Central Bank forecasts for real GDP are correlated with their forecasts for monetary policy. Carrière-Swallow and Marzluf (2023) show that IMF real GDP growth forecast errors are correlated with financial variables.

We test the rationality of multiple international organisations and the Consensus mean for as many economies as possible. In contrast, much of the literature tends to test the rationality of only one or two international organisations. A notable exception is Abreu (2011), who tested the rationality of forecasts for nine advanced economies over 1991 to 2009. A key takeaway from Abreu (2011) is that the departures from rationality tend to be similar across forecasters.^[5] This is useful, but leaves open the possibility that Abreu's findings are specific to these economies during the pre-financial-crisis period. By covering more economies over a longer time period, we can have more confidence that we have identified general features of international organisations' forecasts.

The literature also studies the consequences of departures from rationality for the forecasts. For example, Clements (2022) finds evidence that irrationality in forecasts contributes to persistent differences in accuracy across forecasters. Some literature studies the implications of irrationality for the behaviour of the economy. Bordalo, Gennaioli and Shleifer (2018) show that a model of irrationality called ‘diagnostic expectations’ can explain credit cycles, while Bordalo et al (2021) show that this model can explain asset price bubbles.

2.3 Explaining rejection of rationality

An extensive literature provides explanations for the rejection of the null of rationality in standard forecast rationality tests. Much of that literature relates to consumers, firms or financial market participants. However, the explanations for irrationality at international organisations may be quite different, given that they make forecasts using different methods and face different incentives. Our paper investigates two explanations that are based on specific features of international organisations, and of similar institutions such as central banks and ministries of finance.

The first explanation is that international organisations often make ‘conditional forecasts’, defined as forecasts that are conditional on assumed future paths for a set of explanatory variables. In our paper, we characterise the circumstances under which making conditional forecasts will cause standard rationality tests to be rejected. We show that these tests may be rejected because the assumptions about the explanatory variable may be irrational, or because the ‘response function’ that relates growth to the explanatory variables has certain properties, such as strict convexity or strict concavity.

A related paper, Faust and Wright (2008), develops a method that tests if a set of observed growth forecasts are rational given a set of observed interest rate assumptions. Their approach is useful, as forecasters who make conditional forecasts will be interested to know if they are making rational conditional forecasts. However, their method can only be applied where the assumptions about the explanatory variable are observed, which is not true of some forecasters, and not true of assumptions about qualitative developments such as a war. Moreover, their method can only be used when the relationship between growth and the explanatory variable is linear, which may not be the case for some variables, such as those related to the climate (e.g. temperature). For this reason, researchers are likely to continue running standard rationality tests on conditional forecasts. In such cases, our theoretical results provide guidance on whether the conditional nature of the forecasts can explain the failure of these standard tests.^[6]

The second explanation is that international organisations may be reporting the conditional mode of future growth, rather than the conditional mean. Reporting the conditional mode is rational if one's loss function applies no penalty to being exactly correct (to the published precision of the forecasts, usually 1 decimal place) and applies the same penalty to being incorrect by any amount. If the conditional distribution is symmetric and unimodal, then the mean and mode will coincide, otherwise the mean and mode may differ. Two papers have tried to explain optimistic bias in growth forecasts in a similar way. Firstly, Kangur et al (2019) find that IMF forecasts for the euro area have an optimistic bias. They suggest that the IMF may be making modal forecasts, which will tend to be higher than mean forecasts because the distribution of growth outcomes is negatively skewed. Secondly, Burgess et al (2021) find that the distribution of real GDP per capita growth is negatively skewed in most economies, and that this could in principle explain the bias.^[7] They then show that, empirically, mean growth and median growth tend to be similar, and argue on this basis that the bias in IMF forecasts cannot be fully explained by the skew of growth outcomes.^[8] Our paper advances on these previous efforts in two ways:

We argue that the practice of making modal forecasts could lead to bias (as noted by Kangur et al (2019)), but we additionally argue this practice could cause the failure of rationality tests by causing the forecast errors to be correlated with variables known on the forecast date.
We attempt to quantify the effect of making modal rather than mean forecasts on bias. Burgess et al (2021) did a similar exercise, but they compared the mean with the median instead. However, this provides little information about the effect of making modal forecasts, because even when the mean and median are similar, the mode could be quite different.^[9]

Footnotes

The orthogonality condition rules out linear relationships between forecast errors and variables in the information set. This is weaker than jointly assuming forecast rationality and squared error loss, which rules out any relationship between forecast errors and variables in the information set. [2]

Some papers study international organisations' forecasts for a single country in detail, including Vlah Jerić, Zoričić and Dolinar (2020) for Croatia, Ziembińska (2021) for Poland, Baumgartner (2002) for Austria and Tsuchiya (2016) for Japan. [3]

Mincer and Zarnowitz (1969) regress the actual outcome on an intercept and the forecast, and test if the intercept is 0 and the coefficient on the forecast is 1. This is equivalent to regressing the forecast error on an intercept and forecast and then testing if both the intercept and slope coefficient are 0. [4]

For example, for Spring next-year forecasts by the IMF, EC and OECD, Abreu (2011) rejects unbiasedness for Germany, France and Italy at a 10 per cent level, but not for any of the other six economies. Similarly, for the IMF and EC, Abreu rejects the null of no autocorrelation in the errors for Italy and Spain at a 10 per cent level, but not for other economies. [5]

Lewis and Pain (2015), Engelke, Heinisch and Schult (2019), Lambrias and Page (2019) and Glas and Heinisch (2023) all show that the accuracy of the assumptions affects the accuracy of the growth forecasts, but do not consider how the practice of making conditioning assumptions affects rationality. [6]

Bekaert and Popov (2019) also find negative skewness in most economies, but find positive skewness in many others. They argue that low-income economies may have positive skewness because they experience occasional ‘growth spurts’, such as an increase in growth due to the discovery of oil. [7]

Modal forecasts have also been explored in the context of nominal interest rates forecasting, where the presence of the zero lower bound implies that the distribution of future interest rates is right-skewed, so the mean forecast will exceed the modal forecast (Dison and Elliott 2015; Bauer and Rudebusch 2016). [8]

For example, if growth follows a skew-normal distribution with location 2, scale 1 and shape –5, then the mean will be 1.2, the median 1.3 and the mode 1.6. [9]