REALITY CHECK: FORECASTING GROWTH IN THE MIDDLE EAST AND NORTH AFRICA IN TIMES OF UNCERTAINTY
II.3. The role of growth volatility and data transparency in forecast errors This section employs econometric techniques to explore the strength of the relationship between growth forecast errors and key variables such as data opacity, growth volatility and forecaster type, while accounting for several factors. The empirical specifications are detailed in Appendix II.2. 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 Consensus/ Focus Economics private forecasts (2015–2020). The findings are presented in table X1 in Appendix II.1. The dependent or outcome variable is the absolute growth forecast error (in other words, the inaccuracy of the forecasts). A key variable of interest is the SCI, the proxy capturing the extent of data opacity. Other variables include the log of real GDP per capita, commodity price shocks, internal conflicts, country size measured by total population, and whether the economy is experiencing an economic boom (details provided in Appendix II.2). Year fixed effects (dummy variables capturing the effect of each year of the time period) are included to account for global shocks that are common to all countries in any given year. Results for the World Bank GEP sample are presented in column 1. Column 2 presents the regression results for the IMF WEO sample, and column 3 presents the results for the Consensus/Focus Economics private forecaster sample. Columns 4 through 6 replicate columns 1 through 3, inclusive of the interaction between SCI and the MENA regional dummy variable (that is, comparing the effects of SCI on forecast errors for the MENA region versus the rest of the world). Columns 7 through 9 replicate columns 1 through 3 with the inclusion of growth volatility as an additional independent variable (a covariate). The results in table X1 (columns 4 through 6) indicate that the MENA region has higher absolute forecast errors than the rest of the world, confirming the previous unconditional22 evidence that growth forecasts for MENA are the most inaccurate among world regions. Even after accounting for several covariates, the coefficient for the MENA region dummy variable is positive and statistically significant, at least at the 10 percent level across all three samples. This suggests that the MENA region has higher absolute forecast errors than the rest of the world even after accounting for other factors. Similar findings are obtained in table X2 in which the dependent variable is the simple growth forecast error—here, the MENA region dummy is also positive, indicating growth forecasts in MENA are more optimistic compared to the rest of the world. Growth forecasts are more accurate in countries with better data ecosystems. The quality of the data ecosystem (SCI) is negatively correlated with the absolute growth forecast errors (table X1). This is consistent across World Bank, IMF, and private sector’s forecast errors. The coefficient of private-sector forecasters is statistically insignificant at the 10 percent level but retains a large magnitude despite a small sample size. Forecasters have more and better information in countries with better data ecosystems, leading to lower forecast errors. The coefficient of the interaction between SCI and the MENA region dummy is negative and statistically significant for all three samples (columns 4, 5 and 6), implying that the relationship between the SCI and absolute forecast errors is stronger for the MENA region. Previous analysis linked data quality and data transparency to better economic outcomes in MENA (Arezki and others 2020). The accuracy of growth forecasts is yet another dimension that would benefit from better data ecosystems in the developing MENA region.
22 That is, when no other factors are accounted for.
Chapter II. Forecasting Growth when Data are Opaque
35
