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Nowcasting South African gross domestic product using a suite of statistical models
Authors:Byron Botha  Tim Olds  Geordie Reid  Daan Steenkamp  Rossouw van Jaarsveld
Affiliation:Economic Research Department, South African Reserve Bank, Pretoria, South Africa
Abstract:
Given lags in the release of data, a central bank must ‘nowcast’ current gross domestic product (GDP) using available quarterly or higher frequency data to understand the current state of economic activity. This paper uses various statistical modelling techniques to draw on a large number of series to nowcast South African GDP. We also show that GDP volatility has increased markedly over the last 5 years, making GDP forecasting more difficult. We show that all the models developed, as well as the Reserve Bank's official forecasts, have tended to overestimate GDP growth over this period. However, several of the statistical nowcasting models we present in this paper provide competitive nowcasts relative to the official Reserve Bank and market analysts' nowcasts.
Keywords:forecast evaluation  machine learning  nowcasting
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