forecasters to foresee (Eicher et al., 2019). It is plausible that an optimism bias emerges as a result of forecasters' inability to predict recessions (An et al., 2021). Even as pertinent new information is revealed, forecasters hold onto prior views for far too long (Loungani et al., 2013). Finally, is the year fixed effect, accounting for global shocks, and is the error term. Note that L L ; L L L and tt ττ belong to the information set available to forecasters at the time of forecast (January of each ii,tt ii,tt−1 ii,tt−1 year). A and I εε I are new developments in the year of the forecast LLII ii,tt−1 BBL mm that forecasters might not foresee. Hence, they can explain forecast error. ii,tt ii,tt AA∆L LLFFL LLFFppFFppAA FFppFFI IIL IIppF Many other explanatory variables—such as institutions, polity, informality, and natural disasters—are considered as part of the robustness checks in Section VI. They are not included here as they do not affect the main results and do not have much explanatory power. They also entail a drop in observations in some cases, and thus we opted for a more parsimonious specification. Note that country fixed effects are not included because L L is a slow-moving variable and is largely absorbed into the country fixed effects when included. This is a concern as we are unable to account for all time-invariant country-specific omitted variables. Thus, we account as for many variables in the literature that have been theoretically LLII linked to growth forecast errors.

The volatility of GDP growth rates is another variable that may affect growth forecast errors. Lewis and Pain (2014) found that when forecast errors are adjusted for the significant cross-country variations in average GDP growth rates and GDP growth volatility, differences in forecasting performance among OECD countries tend to diminish. From the late 1970s through the middle of the 2000s, the G7 economies' average absolute forecast error dropped. This is due in part to the moderation of GDP growth volatility. Shorter-term declines may also be a result of better data availability, particularly for near-term developments, and better forecasting methods. These findings are supported by Turner (2016), who also noted that the decrease in GDP growth volatility and possibly improved accessibility and timeliness of national accounts data and other hard indicators are likely to contribute to the trend improvement in current-year forecasting performance in OECD countries over the period 1971–2012. Therefore, we run an additional set of estimations including GDP growth volatility as a covariate, which is measured as the lagged rolling 10-year standard deviation of realized GDP growth rates. Growth volatility is correlated with a number of covariates, and therefore we include it in a separate sect of regressions to uncover what factors may be affecting forecast errors through the growth volatility channel. Additionally, equation 1 is re-estimated with forecast errors as the outcome variable ( F F ) to analyze forecast optimism. An additional adjustment is that the lagged log of change in export commodity prices ( ∆L ) is used in equation 2 instead of its absolute value in equation 1, to capture the direction AAF of the price shock (see equation 2). LLFFL LLFFppFFppAA F F 1 L L 2 L L L 3 ∆L + 4 I I 5 L L L 6 (2) ii,tt ii,tt−1 ii,tt−1 ii,tt AAF = αα+ ββ LLII + ββ LLFF + ββ LLFFL LLFFppFFppAA ii,tt ii,tt−1 ii,tt−1 tt ii,tt ββ FFppFFI IIL IIppF + ββ + ββ BBL mm + ττ + εε

# IV. Main Findings: Growth Volatility, Data Transparency and Forecast Errors

As previously indicated, growth forecast errors are analyzed for three samples: (i) the World Bank’s GEP January forecasts (2010-2020); (ii) the IMF’s WEO January forecasts (2010-2020); and (iii) the January Consensus/Focus Economics private forecasts (2015-2020). Results for the World Bank GEP sample are 13