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Modeling the Dynamics of Inflation Compensation

Author

Listed:
  • Markus Jochmann

    (University of Strathclyde)

  • Gary Koop

    (University of Strathclyde, The Rimini Center for Economic Analysis)

  • Simon M. Potter

    (Federal Reserve Bank of New York)

Abstract

This paper investigates the relationship between short-term and long-term inflation expectations using daily data on inflation compensation. We use a flexible econometric model which allows us to uncover this relationship in a data-based manner. We relate our findings to the issue of whether inflation expectations are anchored, unmoored or contained. Our empirical results indicate no support for either unmoored or firmly anchored inflation expectations. Most evidence indicates that inflation expectations are contained.

Suggested Citation

  • Markus Jochmann & Gary Koop & Simon M. Potter, 2009. "Modeling the Dynamics of Inflation Compensation," Working Paper series 15_09, Rimini Centre for Economic Analysis.
    • Handle: RePEc:rim:rimwps:15_09
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    References listed on IDEAS

    as
    1. Ait-Sahalia, Yacine, 1996. "Testing Continuous-Time Models of the Spot Interest Rate," The Review of Financial Studies, Society for Financial Studies, vol. 9(2), pages 385-426.
    2. Levin, Andrew & Gürkaynak, Refet & Swanson, Eric T., 2006. "Does Inflation Targeting Anchor Long-Run Inflation Expectations? Evidence from Long-Term Bond Yields in the US, UK and Sweden," CEPR Discussion Papers 5808, C.E.P.R. Discussion Papers.
    3. Campbell, J.Y. & Shiller, R.J., 1988. "Stock Prices, Earnings And Expected Dividends," Papers 334, Princeton, Department of Economics - Econometric Research Program.
    4. Koop, Gary & Potter, Simon, 2010. "A flexible approach to parametric inference in nonlinear and time varying time series models," Journal of Econometrics, Elsevier, vol. 159(1), pages 134-150, November.
    5. Fama, Eugene F, 1981. "Stock Returns, Real Activity, Inflation, and Money," American Economic Review, American Economic Association, vol. 71(4), pages 545-565, September.
    6. Jon Faust & Dale W. Henderson, 2004. "Is inflation targeting best-practice monetary policy?," Review, Federal Reserve Bank of St. Louis, vol. 86(Jul), pages 117-144.
    7. James H. Stock & Mark W. Watson, 2007. "Erratum to "Why Has U.S. Inflation Become Harder to Forecast?"," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
    8. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 361-393.
    9. William Dudley & Michelle Steinberg Ezer & Jennifer E. Roush, 2009. "The case for TIPS: an examination of the costs and benefits," Economic Policy Review, Federal Reserve Bank of New York, vol. 15(Jul), pages 1-17.
    10. J. Durbin, 2002. "A simple and efficient simulation smoother for state space time series analysis," Biometrika, Biometrika Trust, vol. 89(3), pages 603-616, August.
    11. repec:bla:jfinan:v:43:y:1988:i:3:p:661-76 is not listed on IDEAS
    12. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    13. Schotman, Peter C. & Schweitzer, Mark, 2000. "Horizon sensitivity of the inflation hedge of stocks," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 301-315, November.
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