Can machine learning algorithms lead to more accurate nowcasts of Singapore's GDP?
This paper investigates if machine learning (ML) models are able to produce accurate real-time nowcasts of Singapore’s GDP. Adopting dynamic factors with the Kalman smoother approach to simulate a realistic nowcasting exercise that incorporates the publication lags of variables, we evaluate the...
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sg-ntu-dr.10356-1473092023-03-05T15:46:25Z Can machine learning algorithms lead to more accurate nowcasts of Singapore's GDP? Cheong, Wei Si Tan, Hong Bing Wise, Vincent WANG, Wei-Siang School of Social Sciences WSWANG@ntu.edu.sg Social sciences::Economic theory This paper investigates if machine learning (ML) models are able to produce accurate real-time nowcasts of Singapore’s GDP. Adopting dynamic factors with the Kalman smoother approach to simulate a realistic nowcasting exercise that incorporates the publication lags of variables, we evaluate the nowcasting accuracy of a suite of ML models, which includes the Elastic Net, Random Forest (RF), Gradient Boosted Trees (GBT), and Support Vector Regression (SVR), alongside simple ensembles. The models are assessed against the autoregressive model (AR) and the dynamic factor model (DFM) which act as benchmarks. In addition, we investigate if the ML models’ variable importance metrics are able to point policymakers towards the underlying economic indicators that would be useful to monitor, to address the common critique of ML models in lacking interpretability. We find that all four ML models were superior to the AR benchmark, but only the linear ML models (SVR & Elastic Net) outperformed the DFM benchmark and were robust in both calm and volatile periods. On the other hand, the tree-based models (RF & GBT) performed well only in calmer periods, which revealed their limitations in extrapolation. The simple ensemble models were also competitive and provided accuracy gains over most ML models individually. We also find that the ML models were able to appropriately assign high importance to variables in a way that conforms with Singapore’s economic structure and the business cycle literature. In sum, we find that the use of ML models helps to improve nowcasts of Singapore’s GDP. Bachelor of Arts in Economics 2021-03-31T07:19:17Z 2021-03-31T07:19:17Z 2021 Final Year Project (FYP) Cheong, W. S., Tan, H. B. & Wise, V. (2021). Can machine learning algorithms lead to more accurate nowcasts of Singapore's GDP?. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/147309 https://hdl.handle.net/10356/147309 en application/pdf Nanyang Technological University |
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Social sciences::Economic theory Cheong, Wei Si Tan, Hong Bing Wise, Vincent Can machine learning algorithms lead to more accurate nowcasts of Singapore's GDP? |
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This paper investigates if machine learning (ML) models are able to produce accurate real-time nowcasts
of Singapore’s GDP. Adopting dynamic factors with the Kalman smoother approach to simulate a
realistic nowcasting exercise that incorporates the publication lags of variables, we evaluate the
nowcasting accuracy of a suite of ML models, which includes the Elastic Net, Random Forest (RF),
Gradient Boosted Trees (GBT), and Support Vector Regression (SVR), alongside simple ensembles. The
models are assessed against the autoregressive model (AR) and the dynamic factor model (DFM) which
act as benchmarks. In addition, we investigate if the ML models’ variable importance metrics are able to
point policymakers towards the underlying economic indicators that would be useful to monitor, to
address the common critique of ML models in lacking interpretability. We find that all four ML models
were superior to the AR benchmark, but only the linear ML models (SVR & Elastic Net) outperformed
the DFM benchmark and were robust in both calm and volatile periods. On the other hand, the tree-based
models (RF & GBT) performed well only in calmer periods, which revealed their limitations in
extrapolation. The simple ensemble models were also competitive and provided accuracy gains over
most ML models individually. We also find that the ML models were able to appropriately assign high
importance to variables in a way that conforms with Singapore’s economic structure and the business
cycle literature. In sum, we find that the use of ML models helps to improve nowcasts of Singapore’s
GDP. |
author2 |
WANG, Wei-Siang |
author_facet |
WANG, Wei-Siang Cheong, Wei Si Tan, Hong Bing Wise, Vincent |
format |
Final Year Project |
author |
Cheong, Wei Si Tan, Hong Bing Wise, Vincent |
author_sort |
Cheong, Wei Si |
title |
Can machine learning algorithms lead to more accurate nowcasts of Singapore's GDP? |
title_short |
Can machine learning algorithms lead to more accurate nowcasts of Singapore's GDP? |
title_full |
Can machine learning algorithms lead to more accurate nowcasts of Singapore's GDP? |
title_fullStr |
Can machine learning algorithms lead to more accurate nowcasts of Singapore's GDP? |
title_full_unstemmed |
Can machine learning algorithms lead to more accurate nowcasts of Singapore's GDP? |
title_sort |
can machine learning algorithms lead to more accurate nowcasts of singapore's gdp? |
publisher |
Nanyang Technological University |
publishDate |
2021 |
url |
https://hdl.handle.net/10356/147309 |
_version_ |
1759857108079083520 |