**Figure 3. GDP Growth Forecast Errors by Geographical and Institution Type**

<!-- Start of picture text --> Panel (A) Panel (B)<br>Absolute Forecast Error Regressions Forecast Error Regressions<br>1.0 1.0<br>0.5 0.5<br>0.0 0.0<br>-0.5 -0.5<br>-1.0 -1.0<br>IMF Forecast Consensus/Focus Regional International IMF Forecast Consensus/Focus Regional International<br>Forecasters Forecasters Forecasters Forecasters Forecasters Forecasters<br>Institution Regression Geographic Regression Institution Regression Geographic Regression<br>Estimated coefficients Estimated coefficients<br><!-- End of picture text --> Source: Authors’ calculations based on the January Consensus/Focus Economics Forecasts, the International Monetary Fund’s January _World Economic Outlook_ (WEO) and the World Bank’s January _Global Economic Prospects_ (GEP). Note: This figure shows the results of two separate regressions for forecasters classified according to their institution type; and classified according to geography. In the institution regressions, the three categories are WB-GEP, IMF-WEO and Consensus/Focus Economics: WB-GEP is the omitted category. In the geographic regressions, the international, regional, and local categorization is based on the proximity of the forecaster’s headquarters to the country being forecasted: “Local Forecasters” is the omitted category. Same-year forecasts in January are used for WB-GEP Forecasts (2010-2020), IMF-WEO Forecasts (2010-2020) and Consensus/Focus Economics Forecasts (2015-2020) for a pooled sample of 132 countries. Bars represent 90 percent confidence intervals _._ 16.6 percent of forecasts in the sample are by local forecasters. 17 percent of forecasts in the sample are by regional forecasters. 66.4 percent of forecasts in the sample are by international forecasters. There are a total of 218 local forecasters, 93 regional forecasters, and 107 international forecasters in the sample.

# V.3 Short-term vs Long-term Forecast Errors

Thus far, the determinants of same-year GDP growth forecast errors have been investigated, but another question to be considered is how the impact of data capacity and transparency matter for longer-term forecasts. To do this, we examine the association between SCI and the World Bank GEP’s one-year ahead GDP growth forecast error (e.g., 2020 growth forecasts are made in January 2019) and two-year ahead GDP growth forecast error (e.g., 2020 growth forecasts are made in January 2018).<sup>13</sup> SCI remains statistically significant with the expected sign, for both one-year ahead forecast errors and absolute forecast errors, with and without growth volatility (See Appendix Table A5). This suggests that better data systems still allow forecasters to paint a more accurate and less biased picture of a more distant future. However, the importance of SCI in the accuracy of one-year ahead forecasts seems smaller than same-year forecasts, as the magnitude of SCI in Appendix Table A5 is smaller than that in Tables 3 and 4 (for example, -1.50 shown in column 2 of Appendix Table A5 versus -3.1 shown in column 1 of Table 4).

The association of SCI and two-year ahead forecast errors is also explored, but at a cost. Data of the World Bank GEP’s two-year ahead GDP growth forecast errors only start in 2015. Despite this limited sample, there are points to be made. SCI remains statistically significant for the two-year ahead forecast errors and absolute forecast errors (Appendix Table A6). Similar to one-year ahead forecasts, the magnitude of

> 13 GEP generally does not provide three-year ahead forecasts.

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