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

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  • Hwee Kwan Chow

    (SMU)

  • Keen Meng Choy

Abstract

A dynamic factor model is applied to a large panel dataset of Singapores macroeconomic variables and global economic indicators with the initial objective of analyzing business cycles in a small open economy. The empirical results suggest that four common factors are present in the quarterly time series, which can broadly be interpreted as world, regional, electronics and domestic economic cycles. The estimated factor model explains well the observed fluctuations in real economic activity and price inflation, leading us to use it in forecasting Singapores business cycles. We find that the forecasts generated by the factors are generally more accurate than the predictions of univariate models and vector autoregressions that employ leading indicators.

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Bibliographic Info

Paper provided by East Asian Bureau of Economic Research in its series Macroeconomics Working Papers with number 22074.

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Date of creation: Jan 2009
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Handle: RePEc:eab:macroe:22074

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Postal: JG Crawford Building #13, Asia Pacific School of Economics and Government, Australian National University, ACT 0200
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Keywords: Business cycle; Dynamic factor model; Forecasting; Singapore;

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  1. Chow, Hwee Kwan & Choy, Keen Meng, 2006. "Forecasting the global electronics cycle with leading indicators: A Bayesian VAR approach," International Journal of Forecasting, Elsevier, vol. 22(2), pages 301-315.
  2. Danthine, Jean-Pierre & Girardin, Michel, 1989. "Business cycles in Switzerland : A comparative study," European Economic Review, Elsevier, vol. 33(1), pages 31-50, January.
  3. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2002. "Do Financial Variables Help Forecasting Inflation and Real Activity in the Euro Area?," CEPR Discussion Papers 3146, C.E.P.R. Discussion Papers.
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  9. Ben Bernanke & Jean Boivin & Piotr S. Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, MIT Press, vol. 120(1), pages 387-422, January.
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  16. Jean Boivin & Serena Ng, 2005. "Understanding and Comparing Factor-Based Forecasts," NBER Working Papers 11285, National Bureau of Economic Research, Inc.
  17. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
  18. Kunhong Kim & Yong-Yil Choi, 1997. "Business cycles in Korea: Is there any stylized feature?," Journal of Economic Studies, Emerald Group Publishing, vol. 24(5), pages 275-293, October.
  19. Garcia-Ferrer, Antonio & Poncela, Pilar, 2002. "Forecasting European GNP Data through Common Factor Models and Other Procedures," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(4), pages 225-44, July.
  20. Norrbin, Stefan C & Schlagenhauf, Don E, 1991. "The Importance of Sectoral and Aggregate Shocks in Business Cycles," Economic Inquiry, Western Economic Association International, vol. 29(2), pages 317-35, April.
  21. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
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