RDP 2026-05: Real GDP Forecasts by International Organisations Appendix A: Detail on Data

A.1 Computing forecast horizons

We define the horizon of a forecast as the number of days from the publication date of the forecast to the end date of the target year. The publication date of a forecast is assumed to be the 15th day of the publication month.^[26] International organisations and Consensus forecast real GDP growth in calendar years for most economies, but forecast growth in fiscal years in others (see Table A1 for a list). For example, WEO forecasts US real GDP growth in calendar years, so in the October 2019 WEO, the end date of the first target year will be 31 December 2019. However, WEO forecasts Indian real GDP growth in fiscal years, so in the October 2019 WEO, the end date of the first target year will be 31 March 2020.

When comparing forecasters or testing the rationality of forecasts, we report results for a set of forecasts with a specific horizon. In the terminology of Clements (1997), we perform ‘rolling event’ regressions rather than ‘fixed event’ regressions, because our regressions are estimated on samples of forecasts with a fixed horizon and a varying forecast date. However, a subtlety is that we round the forecast horizons to ensure we have adequate sample sizes. For example, when we test the rationality of two-year-ahead forecasts by EC, we estimate our regression on all EC forecasts whose horizon in days is between 548 days and 913 days. An alternative to pooling together forecasts with similar horizons is to construct a set of artificial fixed-event forecasts, an approach that has been critiqued by Yetman (2018).

A.2 Computing forecast errors

A.2.1 Source of actual outcomes

For WEO, EO and EC, we compute forecast errors by comparing the institution's forecast with their own actuals. The forecast databases for these institutions provide vintages comprising actual outcomes and forecasts. To distinguish between actuals and forecasts, we need to make an assumption about how long it takes for statistical agencies to publish actual outcomes after the end of the target year. Our assumption is that this takes 90 days. To illustrate, consider WEO vintages of real GDP growth in India. WEO reports Indian real GDP growth over fiscal years that end on 31 March of each year. Growth for fiscal year 2016/17 will be published on 29 June 2017, which is 90 days after the end of the fiscal year. As discussed above, we assume the publication date of a forecast is the 15th day of the publication month. Hence, the June 2017 WEO vintage is assumed to have been published on 15 June 2017, so we assume that the value reported by WEO for 2016/17 is a forecast rather than an actual outcome.

The GEP database also contains both forecasts and actuals, so they need to be distinguished as above. However, the data on actuals has too short a history to enable the calculation of forecast errors. Hence, we compute errors in GEP forecasts by comparing them with WEO actuals. One might be concerned that the forecasts for an economy may be recorded over a different year as the actuals, since they are taken from different sources. Unfortunately, there are some economies for which the GEP forecasts are recorded over different years to the WEO actuals, and we are unable to compute forecast errors in these economies (see Table A1 for the specific economies).

Consensus provides forecasts, but not actuals. Hence, we compute errors in Consensus forecasts by comparing them with WEO actuals. Fortunately, Consensus uses the same years as WEO for all economies where it makes forecasts.

A.2.2 Rationale for use of latest vintage of actual

For all forecasters, we compute errors using the latest vintage of the actual at the time of writing. An alternative approach would have been to take the first vintage of the actual. Revisions to real GDP data can be substantial (Genberg and Martinez 2014a), so this would lead to different results. Our choice of approach rests on two claims:

1. International organisations should aim to forecast the best estimate of true real GDP growth. True growth determines other economic variables and welfare, so a forecast for the best estimate of true growth is the best guide to policy.
2. Later vintages of actuals are better estimates of ‘true’ growth than earlier vintages. A major source of revisions to real GDP data is that the statistical agency receives additional data over time. For example, a statistical agency may produce the first vintage of a component of GDP based solely on survey data, but produce more accurate later vintages using tax data as well. This makes later vintages better estimates of true growth. Another source of revisions is changes to international and national standards for the measurement of GDP. Whether such revisions also make the data a better estimate of true growth is less clear. One view is that the changed standards better approximate some ideal notion of real GDP. Another view is that the later vintages are estimates of a slightly different variable to that which the forecaster had in mind.^[27] In our view, even if one is concerned about changing standards, this second claim would still hold because the later vintages of GDP benefit so much from additional data.

Taken together, these claims imply that international organisations should aim to forecast the latest vintage of the actual, which makes this latest vintage the proper standard by which the accuracy and rationality of their forecasts should be assessed.

A limitation of our approach is that it reduces the comparability of earlier and later errors, since forecasts from earlier vintages will be compared to actuals that have been revised many times, while forecasts from later vintages will be compared to actuals that have been revised only a few times. On balance, however, we judge that it is appropriate to compute errors using the latest vintage of the actuals.

Footnotes

The schedules on which international organisations have published their forecasts has changed over time. We attempted to collect the publication date of each individual vintage for each forecaster, but the information was often unreliable. For example, the OECD provided us with a spreadsheet of publication months. The OECD iLibrary lists precise publication dates, but they often contradict the publication months. [26]

For example, suppose that an economy's standards were revised to say that its real GDP measures should include estimates of production of illegal drugs. This may lead to revisions to the history of real GDP growth for that economy. A forecaster may have made an accurate prediction for growth excluding illicit drugs, but be judged by us to have made a forecast error as its forecast differed from growth including illicit drugs. [27]