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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. 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.
    2. 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.
    3. Boivin, Jean & Giannoni, Marc & Mihov, Ilian, 2007. "Sticky Prices and Monetary Policy: Evidence from Disaggregated US Data," CEPR Discussion Papers 6101, C.E.P.R. Discussion Papers.
    4. Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez & Thomas J. Sargent, 2005. "A,B,C's (and D's)'s for Understanding VARS," Levine's Bibliography 172782000000000096, UCLA Department of Economics.
    5. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2004. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," NBER Working Papers 10220, National Bureau of Economic Research, Inc.
    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. Jörg Breitung & Sandra Eickmeier, 2006. "Dynamic factor models," AStA Advances in Statistical Analysis, Springer, vol. 90(1), pages 27-42, March.
    8. International Monetary Fund, 2009. "Interest Rate Liberalization in China," IMF Working Papers 09/171, International Monetary Fund.
    9. 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.
    10. 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.
    11. Marcellino, Massimiliano & Stock, James H & Watson, Mark W, 2005. "A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series," CEPR Discussion Papers 4976, C.E.P.R. Discussion Papers.
    12. Moench, Emanuel, 2008. "Forecasting the yield curve in a data-rich environment: A no-arbitrage factor-augmented VAR approach," Journal of Econometrics, Elsevier, vol. 146(1), pages 26-43, September.
    13. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
    14. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    15. 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.
    16. Bernanke, Ben S. & Mihov, Ilian, 1998. "The liquidity effect and long-run neutrality," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 49(1), pages 149-194, December.
    17. 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.
    18. 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.
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