Growth Forecasts Using Time Series and Growth Models

It is difficult to choose the best model for forecasting real per capita GDP for a particular country or group of countries. This study suggests potential gains from combining time series and growth-regression-based approaches to forecasting.Kraay and Monokroussos consider two alternative methods of forecasting real per capita GDP at various horizons:- Univariate time series models estimated country by country.- Cross-country growth regressions.They evaluate the out-of-sample forecasting performance of both approaches for a large sample of industrial and developing countries.They find only modest differences between the two approaches. In almost all cases, differences in median (across countries) forecast performance are small relative to the large discrepancies between forecasts and actual outcomes.Interestingly, the performance of both models is similar to that of forecasts generated by the World Bank's Unified Survey.The results do not provide a compelling case for one approach over another, but they do indicate that there are potential gains from combining time series and growth-regression-based forecasting approaches.This paper - a product of Macroeconomics and Growth, Development Research Group - is part of a larger effort in the group to improve the understanding of economic growth. The authors may be contacted at akraay@worldbank.org or gmonokroussos@worldbank.org