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Making a Weak Instrument Set Stronger: Factor-Based Estimation of the Taylor Rule

  • Harun Mirza


  • Lidia Storjohann


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    The problem of weak identification has recently attracted attention in the analysis of structural macroeconomic models. Using robust methods can result in large confidence sets making inference difficult. We overcome this problem in the analysis of a forward-looking Taylor rule by seeking stronger instruments. We suggest exploiting information from a large macroeconomic data set by generating factors and using them as additional instruments. This approach results in a stronger instrument set and hence smaller weak-identification robust confidence sets. It allows us to conclude that there has been a shift in monetary policy from the pre-Volcker regime to the Volcker-Greenspan tenure.

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    Paper provided by University of Bonn, Germany in its series Bonn Econ Discussion Papers with number bgse13_2012.

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    Length: 22
    Date of creation: Dec 2011
    Date of revision:
    Handle: RePEc:bon:bonedp:bgse13_2012
    Contact details of provider: Postal: Bonn Graduate School of Economics, University of Bonn, Adenauerallee 24 - 26, 53113 Bonn, Germany
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    1. Fabio Canova & Luca Sala, 2005. "Back to square one: Identification issues in DSGE models," Economics Working Papers 927, Department of Economics and Business, Universitat Pompeu Fabra, revised Sep 2006.
    2. Taylor, John B., 1998. "The Robustness and Efficiency of Monetary Policy Rules as Guidelines for Interest Rate Setting by the European Central Bank," Seminar Papers 649, Stockholm University, Institute for International Economic Studies.
    3. John B. Taylor, 1999. "A Historical Analysis of Monetary Policy Rules," NBER Chapters, in: Monetary Policy Rules, pages 319-348 National Bureau of Economic Research, Inc.
    4. 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.
    5. John B. Taylor, 1999. "Monetary Policy Rules," NBER Books, National Bureau of Economic Research, Inc, number tayl99-1, July.
    6. Bai, Jushan & Ng, Serena, 2010. "Instrumental Variable Estimation In A Data Rich Environment," Econometric Theory, Cambridge University Press, vol. 26(06), pages 1577-1606, December.
    7. Olivier Coibion & Yuriy Gorodnichenko, 2010. "Monetary Policy, Trend Inflation and the Great Moderation:An Alternative Interpretation," Working Papers 94, Department of Economics, College of William and Mary.
    8. Troy Davig & Eric M. Leeper, 2006. "Generalizing the Taylor Principle," Caepr Working Papers 2006-001, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington.
    9. Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Econometric Society World Congress 2000 Contributed Papers 1504, Econometric Society.
    10. 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.
    11. Sophocles Mavroeidis, 2004. "Weak Identification of Forward-looking Models in Monetary Economics," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(s1), pages 609-635, 09.
    12. Ben S. Bernanke & Jean Boivin, 2001. "Monetary Policy in a Data-Rich Environment," NBER Working Papers 8379, National Bureau of Economic Research, Inc.
    13. Jean Boivin & Marc P. Giannoni, 2003. "Has Monetary Policy Become More Effective?," NBER Working Papers 9459, National Bureau of Economic Research, Inc.
    14. Thomas Lubik & Frank Schorfheide, 2002. "Testing for Indeterminacy:An Application to U.S. Monetary Policy," Economics Working Paper Archive 480, The Johns Hopkins University,Department of Economics, revised Jun 2003.
    15. Donald W. K. Andrews & Marcelo J. Moreira & James H. Stock, 2006. "Optimal Two-Sided Invariant Similar Tests for Instrumental Variables Regression," Econometrica, Econometric Society, vol. 74(3), pages 715-752, 05.
    16. Alexei Onatski, 2009. "Testing Hypotheses About the Number of Factors in Large Factor Models," Econometrica, Econometric Society, vol. 77(5), pages 1447-1479, 09.
    17. Atsushi Inoue & Barbara Rossi, 2011. "Identifying the Sources of Instabilities in Macroeconomic Fluctuations," The Review of Economics and Statistics, MIT Press, vol. 93(4), pages 1186-1204, November.
    18. John B. Taylor, 1999. "Introduction to "Monetary Policy Rules"," NBER Chapters, in: Monetary Policy Rules, pages 1-14 National Bureau of Economic Research, Inc.
    19. 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.
    20. Seung C. Ahn & Alex R. Horenstein, 2013. "Eigenvalue Ratio Test for the Number of Factors," Econometrica, Econometric Society, vol. 81(3), pages 1203-1227, 05.
    21. Sophocles Mavroeidis, 2010. "Monetary Policy Rules and Macroeconomic Stability: Some New Evidence," American Economic Review, American Economic Association, vol. 100(1), pages 491-503, March.
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