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

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Author Info

  • 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.

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File URL: http://www.ntu.edu.sg/hss2/egc/wp/2008/2008-02.pdf
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Bibliographic Info

Paper provided by Nanyang Technolgical University, School of Humanities and Social Sciences, Economic Growth centre in its series Economic Growth centre Working Paper Series with number 0802.

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Length: 26 pages
Date of creation: Feb 2008
Date of revision:
Handle: RePEc:nan:wpaper:0802

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Web page: http://egc.hss.ntu.edu.sg/
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Related research

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

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References

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  1. Michael ARTIS & Anindya BANERJEE & Massimiliano MARCELLINO, 2001. "Factor Forecasts for the UK," Economics Working Papers ECO2001/15, European University Institute.
  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. 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.
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  7. Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2006. "A quasi maximum likelihood approach for large approximate dynamic factor models," Working Paper Series 0674, European Central Bank.
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  10. Schumacher, Christian, 2005. "Forecasting German GDP using alternative factor models based on large datasets," Discussion Paper Series 1: Economic Studies 2005,24, Deutsche Bundesbank, Research Centre.
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  12. Kose, M. Ayhan, 2002. "Explaining business cycles in small open economies: 'How much do world prices matter?'," Journal of International Economics, Elsevier, vol. 56(2), pages 299-327, March.
  13. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 1999. "The Generalized Dynamic Factor Model: Identification and Estimation," CEPR Discussion Papers 2338, C.E.P.R. Discussion Papers.
  14. Stock, J.H. & Watson, M.W., 1989. "New Indexes Of Coincident And Leading Economic Indicators," Papers 178d, Harvard - J.F. Kennedy School of Government.
  15. M. Ayhan Kose & Christopher Otrok & Charles H. Whiteman, 2003. "International Business Cycles: World, Region, and Country-Specific Factors," American Economic Review, American Economic Association, vol. 93(4), pages 1216-1239, September.
  16. 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.
  17. 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.
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