FORECASTING SINGAPORE’s ECONOMY USING STATISTICAL LEARNING AND FACTOR MODELS

Benedict Foo, Deng Yao Koh, Juan Pang Tan and Wenjie Wang
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Benedict Foo: Division of Economics, School of Social Sciences, Nanyang Technological University, Singapore
Deng Yao Koh: Division of Economics, School of Social Sciences, Nanyang Technological University, Singapore
Juan Pang Tan: Division of Economics, School of Social Sciences, Nanyang Technological University, Singapore
Wenjie Wang: Division of Economics, School of Social Sciences, Nanyang Technological University, Singapore

The Singapore Economic Review (SER), 2023, vol. 68, issue 02, 319-353

Abstract: We evaluate the performance of the penalized vector autoregression (VAR), diffusion index (DI), and regression tree-based ensemble learning models to forecast Singapore’s macroeconomy using high-dimensional data. Our dataset consists of 220 monthly time series that capture the economy of Singapore and 20 monthly times series that capture the global economic environment. We find that the penalized VAR model and the ensemble learning model give an outstanding performance in both short and long horizons. On the other hand, the performance of the DI model depends crucially on the methods to select the number of factors. In particular, a conventional selection method may overestimate the true number of factors and thus deteriorate the forecasting performance of the DI model. Additionally, the VAR and DI models may utilize different information in forecasting.

Keywords: Vector autoregression; diffusion index; statistical learning; forecasting; Singapore economy (search for similar items in EconPapers)
JEL-codes: C32 C38 C53 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0217590822500655

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