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Were Fed’s active monetary policy actions necessary?

  • Pang, Iris Ai Jao
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    This work applies the two-stage Factor Augmented Vector Autoregression (FAVAR) developed by Bernanke, Boivin and Eliasz (2005) to investigate the appropriateness of frequent monetary policy actions that involve frequent adjustments of the policy interest rate in a prolonged manner. From time to time there are claims that the Federal Reverse Bank cut or raised the fed funds rate too frequently. This raises the concern that the Federal Reserve Bank mistakenly cut interest rate for too long and too frequently and then paused too short and raised rate again to “undo” the previous unnecessary interest rate cut or vice versa. To verify if such a claim is valid, we generate hypothetical scenarios assuming that the Federal Reserve Bank had shortened the time period of active monetary policies and lengthened the period of a pause. Then, we compare economic activities implied by impulse response functions from hypothetical scenarios with those generated from actual fed policies under the record of Alan Greenspan (1987-2006). We find that a less active monetary policy approach could control inflation with less negative impact on real economic activities, and major economic variables would be less volatile in a 48-month horizon. The investigation provides insights on the implementation of monetary policies not only for the U.S., but also for all central banks that control interest rates as their major monetary policy tool.

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    File URL: http://mpra.ub.uni-muenchen.de/32496/1/MPRA_paper_32496.pdf
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    Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 32496.

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    Date of creation: 10 May 2010
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    Handle: RePEc:pra:mprapa:32496
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    1. Mönch, Emanuel, 2005. "Forecasting the yield curve in a data-rich environment: a no-arbitrage factor-augmented VAR approach," Working Paper Series 0544, European Central Bank.
    2. Mario Forni & Marc Hallin & Lucrezia Reichlin & Marco Lippi, 2000. "The generalised dynamic factor model: identification and estimation," ULB Institutional Repository 2013/10143, ULB -- Universite Libre de Bruxelles.
    3. Jean Boivin & Marc Giannoni & Ilian Mihov, 2007. "Sticky Prices and Monetary Policy: Evidence from Disaggregated U.S. Data," NBER Working Papers 12824, National Bureau of Economic Research, Inc.
    4. Jushan Bai & Serena Ng, 2006. "Confidence Intervals for Diffusion Index Forecasts and Inference for Factor-Augmented Regressions," Econometrica, Econometric Society, vol. 74(4), pages 1133-1150, 07.
    5. 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.
    6. Bernanke, Ben S. & Boivin, Jean, 2003. "Monetary policy in a data-rich environment," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 525-546, April.
    7. Massimiliano Marcellino & James Stock & Mark Watson, 2005. "A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series," Working Papers 285, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    8. 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.
    9. Breitung, Jörg & Eickmeier, Sandra, 2005. "Dynamic factor models," Discussion Paper Series 1: Economic Studies 2005,38, Deutsche Bundesbank, Research Centre.
    10. Masahiko Shibamoto, 2007. "An Analysis Of Monetary Policy Shocks In Japan: A Factor Augmented Vector Autoregressive Approach," The Japanese Economic Review, Japanese Economic Association, vol. 58(4), pages 484-503.
    11. International Monetary Fund, 2009. "Interest Rate Liberalization in China," IMF Working Papers 09/171, International Monetary Fund.
    12. Jesus Fernandez-Villaverde & Juan F. Rubio-Ramirez & Thomas J. Sargent, 2005. "A, B, C’s (And D’s) For Understanding VARS," PIER Working Paper Archive 05-018, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    13. 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.
    14. 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.
    15. Ben S. Bernanke & Ilian Mihov, 1998. "The Liquidity Effect and Long-Run Neutrality," NBER Working Papers 6608, National Bureau of Economic Research, Inc.
    16. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
    17. Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Econometric Society World Congress 2000 Contributed Papers 1504, Econometric Society.
    18. Shibamoto, Masahiko, 2008. "The estimation of monetary policy reaction function in a data-rich environment: The case of Japan," Japan and the World Economy, Elsevier, vol. 20(4), pages 497-520, December.
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