Growth forecasts using time series and growth models
AbstractThe authors consider two alternative methods of forecasting real per capita GDP at various horizons: 1) univariate time series models estimated country by country; and 2) 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.
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Bibliographic InfoPaper provided by The World Bank in its series Policy Research Working Paper Series with number 2224.
Date of creation: 30 Nov 1999
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Statistical&Mathematical Sciences; Economic Theory&Research; Scientific Research&Science Parks; Educational Technology and Distance Education; Public Health Promotion; Economic Forecasting; Economic Theory&Research; Achieving Shared Growth; Scientific Research&Science Parks; Science Education;
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