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Forecasting Business Cycles in a Small Open Economy: A Dynamic Factor Model for Singapore

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  • Hwee Kwan Chow

    (School of Economics and Social Sciences, Singapore Management University, Singapore)

  • Keen Meng Choy

    (Department of Economics, Nanyang Technological University, Singapore)

Abstract

We apply multivariate statistical methods to a large dataset of Singapore’s macroeconomic variables and global economic indicators with the objective of forecasting business cycles in a small open economy. The empirical results suggest that three common factors are present in the time series at the quarterly frequency, which can be interpreted as world, regional and domestic economic cycles. This leads us to estimate a factor-augmented vector autoregressive (FAVAR) model for the purpose of optimally forecasting real economic activity in Singapore. By taking explicit account of the common factor dynamics, we find that iterative forecasts generated by this model are significantly more accurate than direct multi-step predictions based on the identified factors as well as forecasts from univariate and vector autoregressions.

Suggested Citation

  • Hwee Kwan Chow & Keen Meng Choy, 2008. "Forecasting Business Cycles in a Small Open Economy: A Dynamic Factor Model for Singapore," Economic Growth Centre Working Paper Series 0802, Nanyang Technological University, School of Social Sciences, Economic Growth Centre.
    • Handle: RePEc:nan:wpaper:0802
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    References listed on IDEAS

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    Cited by:

    1. Hwee Kwan Chow & Keen Meng Choy, 2009. "Monetary Policy And Asset Prices In A Small Open Economy: A Factor-Augmented Var Analysis For Singapore," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 5(01), pages 1-23.
    2. Hyun Hak Kim, 2013. "Forecasting Macroeconomic Variables Using Data Dimension Reduction Methods: The Case of Korea," Working Papers 2013-26, Economic Research Institute, Bank of Korea.

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    Keywords

    business cycles; principal components; dynamic factor model; factor-augmented VAR; forecasting; Singapore;
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