Forecasting Singapore GDP using the SPF data
Tian Xie () and
Jun Yu ()
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Tian Xie: Shanghai University of Finance and Economics
No 17-2020, Economics and Statistics Working Papers from Singapore Management University, School of Economics
In this article, we use econometric methods, machine learning methods, and a hybrid method to forecast the GDP growth rate in Singapore based on the Survey of Professional Forecasters (SPF). We compare the performance of these methods with the sample median used by the Monetary Authority of Singapore (MAS). It is shown that the relationship between the actual GDP growth rates and the forecasts from individual professionals is highly nonlinear and non-additive, making it hard for all linear methods and the sample median to perform well. It is found that the hybrid method performs the best, reducing the mean squared forecast error (MSFE) by about 50% relative to that of the sample median.
Pages: 12 pages
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for and nep-sea
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Persistent link: https://EconPapers.repec.org/RePEc:ris:smuesw:2020_017
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