Forecast Errors

Appendix II.2. Estimating the Relationship between Statistical Capacity and the Magnitude of GDP Growth Forecast Errors

The relationship between statistical capacity and the magnitude of forecast errors is explored using two models across three different samples of forecasters. The first sample is from the World Bank’s Global Economic Prospects January Forecasts (GEP), the second sample is from the International Monetary Fund’s World Economic Outlook January Forecasts (WEO). Both samples cover the same 126 countries from 2010 to 2020. The third sample is the Consensus/Focus Economics Forecasts, covering 56 countries from 2015 to 2020. A panel model is estimated using the absolute value of forecast error of current year forecasts as the outcome variable regressed on the lagged log of statistical capacity and other covariates as presented in equation (AII.2.1):

AbsFcstErri,t = α + β1 LogSCIi,t–1 + β2 LogGDPpci,t–1 + β3 AbsLogExpPricesi,t + β4 InternalConflicti,t–1 + β5 LogPopi,t–1 + β6 Boomi,t–1 + τt + εi,t (AII.2.1)

where the dependent variable is the absolute value of forecast errors (AbsFcstErr), defined as forecasted growth minus actual growth. A negative forecast error signifies that forecasted growth was below actual growth. The main regressor is the log of lagged statistical capacity (LogSCI), as measured by the World Bank Statistical Capacity Indicator (SCI). Other explanatory variables include log lagged GDP per capita (LogGDPpc); the absolute value of log change in export commodity prices (AbsLogExpPrices), which captures both exposure to commodity exports and the fluctuations of the international prices); an internal conflict dummy (InternalConflict) which is as defined using the UCDP-Prio Dataset); lagged log of total population (LogPop) and lagged dummy variable for economic booms (Boom, a dummy variable if growth is greater than or equal to previous 10-year median growth). Finally, τt is the year fixed effect, and εi,t is the error term. In an additional specification growth volatility, which is measured as the standard deviation of growth over the preceding 10 years, is included.1 Many other explanatory variables—such as institutions, polity, informality, and natural disasters—were considered but, because they have smaller explanatory power, were not included to keep the model as simple as possible.

Alternatively, equation A1 is re-estimated with simple forecast error as the outcome variable (FcstErr) to analyze forecast optimism and pessimism. An additional adjustment is that the lagged log of change in export commodity prices (LogExpPrices) is used in equation AII.2.2 instead of its absolute value in equation A1.

FcstErri,t = α + β1 LogSCIi,t–1 + β2 LogGDPpci,t–1 + β3 LogExpPricesi,t + β4 InternalConflicti,t–1 + β5 LogPopi,t–1 + β6 Boomi,t–1 + τt + εi,t (AII.2.2)

Across all specifications and samples, the results show a negative relationship between statistical capacity and the magnitude and sign of forecast errors.

1 Note that country fixed effects are not included in these estimations largely because the statistical capacity indicator has limited variation over time.