expect the absolute spread between growth forecasts and actual growth to rise following a discovery since it may simply be harder to forecast growth after such a large external shock. In contrast, we would not expect a result significantly different from zero for our measure of the simple growth differential- here any over-estimate should be cancelled by under-estimates of the true rate of growth across our global sample.

||(1)<br>Abs dif|(2)<br>Abs dif|(3)<br>Abs dif|(4)<br>Abs dif|(5)<br>Abs dif|
|---|---|---|---|---|---|
|lag<br>~~d~~isco|0.200|0.399|0.221|0.132|0.464<sup>_∗_</sup>|
||(0.197)|(0.277)|(0.201)|(0.234)|(0.267)|
|Fixed efects|Country, Year|Country Year|Country, Year|Country Year|Country, Year|
|Sample|All sample|Weak institutions|Strong institutions|Low and Middle income|High income|
|Obs- Country Year)|3781|1950|857|2647|1134|
|R-sq|0.02|0.03|0.05|0.03|0.13|

Standard errors clustered at country level _∗ p <_ 0 _._ 10, _∗∗ p <_ 0 _._ 05, _∗∗∗ p <_ 0 _._ 01 Table 7: Absolute forecast error, different samples, five year cumulative.

Table 7 presents our results. We find that for the global sample there is no evidence for an increase in the imprecision of the forecasts (absolute error) after discovery. The imprecision error increases significantly in countries with strong institutions, on which we found no evidence of over-forecasting relative to actual growth (simple growth differential - shown in Table 6). In countries with weak institutions, there is no consistent increase in imprecision, but forecasts are more optimistic after discovery relative to actual growth (Table 5).

**Persistent over-forecasting** As shown in the descriptive statistics (Table 1), WEO forecasts are on average larger than actual growth rates on the global sample. The Independent Evaluation Office’s review of WEO forecasts estimated there is a statistically significant persistent over-forecasting of 0.3 percentage point across all countries and year (Genberg and Martinez, 2014). Previous research has also demonstrated that that persistent over-forecasting is not a unique to the IMF. Its widespread occurrence has been documented across forecasts prepared by national governments (Frankel (2011) and Frankel and Schreger (2012)), central banks (Alessi et al. (2014) and Lansing and Pyle (2015)), multilateral institutions (Genberg and Martinez, 2014) and those of the private sector such as the Consensus Forecast (Loungani, 2001). Studies have also found similar levels of efficiency forecasts and a near-perfect collinearity between these sources (Loungani (2001) and Timmermann and Granger (2004)).

Frankel (2011) postulates that over-optimism in official government forecasts explains why governments may have adopted overly pro-cyclical fiscal policy and run excessive budget deficits in boom times. Cimadomo (2012) finds that pro-cyclicality can also be fueled by biased GDP data available in real time, which is then subject to subsequent revisions. In response to these papers, Avellan and Vuletin (2015) argue that pro-cyclicality cannot be explained by biases in forecasts and data revisions alone, it is being enabled and aggravated by weak institutions.

Our estimation technique is not contaminated by any such global over-forecasting effect since we measure variation of forecasting errors within countries across time. Any ‘average’ IMF bias would be captured in our constant term. If persistent over-forecasting is worse in certain contexts, this is handled via 19