Publication Type
Working Paper
Version
Publisher’s Version
Publication Date
7-2020
Abstract
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.
Discipline
Econometrics
Research Areas
Econometrics
First Page
1
Last Page
11
City or Country
Singapore
Embargo Period
7-26-2020
Citation
XIE, Tian and Yu, Jun.
Forecasting Singapore GDP using the SPF data. (2020). 1-11.
Available at: https://ink.library.smu.edu.sg/soe_research/2396
Copyright Owner and License
Singapore Management University
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.