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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.

Suggested Citation

  • Hwee Kwan Chow & Keen Meng Choy, 2009. "Analyzing and Forecasting Business Cycles in a Small Open Economy : A Dynamic Factor Model for Singapore," Macroeconomics Working Papers 22074, East Asian Bureau of Economic Research.
    • Handle: RePEc:eab:macroe:22074
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    1. Danthine, Jean-Pierre & Girardin, Michel, 1989. "Business cycles in Switzerland : A comparative study," European Economic Review, Elsevier, vol. 33(1), pages 31-50, January.
    2. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2003. "Do financial variables help forecasting inflation and real activity in the euro area?," Journal of Monetary Economics, Elsevier, vol. 50(6), pages 1243-1255, September.
    3. Englund, Peter & Persson, Torsten & Svensson, Lars E. O., 1992. "Swedish business cycles: 1861-1988," Journal of Monetary Economics, Elsevier, vol. 30(3), pages 343-371, December.
    4. Danny Quah & Thomas J. Sargent, 1993. "A Dynamic Index Model for Large Cross Sections," NBER Chapters, in: Business Cycles, Indicators, and Forecasting, pages 285-310, National Bureau of Economic Research, Inc.
    5. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    6. Christian Schumacher, 2007. "Forecasting German GDP using alternative factor models based on large datasets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(4), pages 271-302.
    7. 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.
    8. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    9. 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 317-335, April.
    10. Michael Artis & Anindya Banerjee & Massimiliano Marcellino, "undated". "Factor forecasts for the UK," Working Papers 203, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    11. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1.
    12. repec:bla:ecorec:v:70:y:1994:i:208:p:56-73 is not listed on IDEAS
    13. Guglielmo Maria Caporale, 1997. "Sectoral shocks and business cycles: a disaggregated analysis of output fluctuations in the UK," Applied Economics, Taylor & Francis Journals, vol. 29(11), pages 1477-1482.
    14. Long, John B, Jr & Plosser, Charles I, 1983. "Real Business Cycles," Journal of Political Economy, University of Chicago Press, vol. 91(1), pages 39-69, February.
    15. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(1), pages 387-422.
    16. Jean Boivin & Serena Ng, 2005. "Understanding and Comparing Factor-Based Forecasts," International Journal of Central Banking, International Journal of Central Banking, vol. 1(3), December.
    17. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    18. James H. Stock & Mark W. Watson, 1993. "Business Cycles, Indicators, and Forecasting," NBER Books, National Bureau of Economic Research, Inc, number stoc93-1.
    19. Bai, Jushan & Ng, Serena, 2007. "Determining the Number of Primitive Shocks in Factor Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 52-60, January.
    20. Stock, James H. & Watson, Mark W. (ed.), 1993. "Business Cycles, Indicators, and Forecasting," National Bureau of Economic Research Books, University of Chicago Press, edition 1, number 9780226774886.
    21. Kunhong Kim & A. Buckle & V. B. Hall, 1994. "Key Features of New Zealand Business Cycles," The Economic Record, The Economic Society of Australia, vol. 70(208), pages 56-72, March.
    22. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    23. 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.
    24. 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.
    25. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    26. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409, National Bureau of Economic Research, Inc.
    27. 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.
    28. 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 225-244, July.
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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. Mariano, Roberto S. & Ozmucur, Suleyman, 2015. "High-Mixed-Frequency Dynamic Latent Factor Forecasting Models for GDP in the Philippines/Modelos de factores dinámicos latentes con datos mixtos de alta frecuencia aplicados a la predicción del PIB en," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 33, pages 451-462, Mayo.
    3. Mendoza, Liu & Morales, Daniel, 2013. "Construyendo un índice coincidente de recesión: Una aplicación para la economía peruana," Revista Estudios Económicos, Banco Central de Reserva del Perú, issue 26, pages 81-100.
    4. Mendoza, Liu & Morales, Daniel, 2012. "Constructing a real-time coincident recession index: an application to the Peruvian economy," Working Papers 2012-020, Banco Central de Reserva del Perú.
    5. Mapa, Dennis S. & Simbulan, Maria Christina, 2014. "Analyzing and Forecasting Movements of the Philippine Economy using the Dynamic Factor Models (DFM)," MPRA Paper 54478, University Library of Munich, Germany.
    6. Hwee Kwan Chow & Yijie Fei & Daniel Han, 2023. "Forecasting GDP with many predictors in a small open economy: forecast or information pooling?," Empirical Economics, Springer, vol. 65(2), pages 805-829, August.
    7. Kong Yam Tan & Tilak Abeysinghe & Khee Giap Tan, 2015. "Shifting Drivers of Growth: Policy Implications for ASEAN-5," Asian Economic Papers, MIT Press, vol. 14(1), pages 157-173, Winter/Sp.

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    More about this item

    Keywords

    Business cycle; Dynamic factor model; Forecasting; Singapore;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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