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Monetary Policy and Asset Prices in a Small Open Economy: A Factor-Augmented VAR Analysis for Singapore

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  • Hwee Kwan Chow

    ()
    (School of Economics, Singapore Management University)

  • Keen Meng Choy

    ()
    (School of Humanities and Social Sciences, Nanyang Technological University)

Abstract

The ongoing global financial turmoil has revived the question of whether central bankers ought to tighten monetary policy preemptively in order to head off asset price misalignments before a sudden crash triggers financial instability. This study explores the issue of the appropriate monetary policy response to asset price swings in the small open economy of Singapore. Empirical analysis of monetary policy based on standard VAR models, unfortunately, is often hindered by the use of sparse information sets. To better reflect the extensive information monitored by Singapore’s central bank, including global economic indicators, we augment a monetary VAR model with common factors extracted from a large panel dataset spanning 122 economic time series and the period 1980q1 to 2008q2. The resulting FAVAR model is used to assess the impact of monetary policy shocks on residential property and stock prices. Impulse response functions and variance decompositions suggest that monetary policy can potentially be used to lean against asset price booms in Singapore.

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Bibliographic Info

Paper provided by Singapore Management University, School of Economics in its series Working Papers with number 11-2009.

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Length: 37 pages
Date of creation: Oct 2009
Date of revision:
Publication status: Published in SMU Economics and Statistics Working Paper Series
Handle: RePEc:siu:wpaper:11-2009

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Keywords: Monetary Policy; Asset Prices; Dynamic Factors; Vector Autoregression;

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  1. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
  2. 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.
  3. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-36, July.
  4. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
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  6. 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.
  7. Massimiliano Marcellino & Carlo A. Favero & Francesca Neglia, 2005. "Principal components at work: the empirical analysis of monetary policy with large data sets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(5), pages 603-620.
  8. Sims, Christopher A., 1992. "Interpreting the macroeconomic time series facts : The effects of monetary policy," European Economic Review, Elsevier, vol. 36(5), pages 975-1000, June.
  9. Cushman, David O. & Zha, Tao, 1997. "Identifying monetary policy in a small open economy under flexible exchange rates," Journal of Monetary Economics, Elsevier, vol. 39(3), pages 433-448, August.
  10. 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.
  11. Donald L. Kohn, 2006. "Monetary policy and asset prices: a speech at “Monetary Policy: a Journey from Theory to Practice,” a European Central Bank Colloquium held in honor of Otmar Issing, Frankfurt, Germany, March 16, ," Speech 174, Board of Governors of the Federal Reserve System (U.S.).
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