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Unbiased estimate of dynamic term structure models

Author

Listed:
  • Michael D. Bauer
  • Glenn D. Rudebusch
  • Jing (Cynthia) Wu

Abstract

Affine dynamic term structure models (DTSMs) are the standard finance representation of the yield curve. However, the literature on DTSMs has ignored the coefficient bias that plagues estimated autoregressive models of persistent time series. We introduce new simulation-based methods for reducing or even eliminating small-sample bias in empirical affine Gaussian DTSMs. With these methods, we show that conventional estimates of DTSM coefficients are severely biased, which results in misleading estimates of expected future short-term interest rates and long-maturity term premia. Our unbiased DTSM estimates imply risk-neutral rates and term premia that are more plausible from a macro-finance perspective.

Suggested Citation

  • Michael D. Bauer & Glenn D. Rudebusch & Jing (Cynthia) Wu, 2011. "Unbiased estimate of dynamic term structure models," Working Paper Series 2011-12, Federal Reserve Bank of San Francisco.
  • Handle: RePEc:fip:fedfwp:2011-12
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    References listed on IDEAS

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    3. Christensen, Jens H.E. & Diebold, Francis X. & Rudebusch, Glenn D., 2011. "The affine arbitrage-free class of Nelson-Siegel term structure models," Journal of Econometrics, Elsevier, pages 4-20.
    4. Jens H. E. Christensen & Francis X. Diebold & Glenn D. Rudebusch, 2009. "An arbitrage-free generalized Nelson--Siegel term structure model," Econometrics Journal, Royal Economic Society, vol. 12(3), pages 33-64, November.
    5. Engsted, Tom & Pedersen, Thomas Q., 2012. "Return predictability and intertemporal asset allocation: Evidence from a bias-adjusted VAR model," Journal of Empirical Finance, Elsevier, vol. 19(2), pages 241-253.
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    7. Rudebusch, Glenn D, 1992. "Trends and Random Walks in Macroeconomic Time Series: A Re-examination," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 33(3), pages 661-680, August.
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    11. Lutz Kilian, 1998. "Small-Sample Confidence Intervals For Impulse Response Functions," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 218-230, May.
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    Cited by:

    1. Yun, Tack & Kim, Jinsook & Ko, Eunmi, 2012. "The Role of Bounded Rationality in Macro-Finance Affine Term-Structure Models," MPRA Paper 44212, University Library of Munich, Germany.
    2. Michael D. Bauer & Glenn D. Rudebusch, 2014. "The Signaling Channel for Federal Reserve Bond Purchases," International Journal of Central Banking, International Journal of Central Banking, vol. 10(3), pages 233-289, September.
    3. Borgy, V. & Laubach, T. & M├ęsonnier, J-S. & Renne, J-P., 2011. "Fiscal Sustainability, Default Risk and Euro Area Sovereign Bond Spreads Markets," Working papers 350, Banque de France.
    4. Gregory H. Bauer & Antonio Diez de los Rios, 2012. "An International Dynamic Term Structure Model with Economic Restrictions and Unspanned Risks," Staff Working Papers 12-5, Bank of Canada.
    5. Daniela Osterrieder & Peter C. Schotman, 2012. "The Volatility of Long-term Bond Returns: Persistent Interest Shocks and Time-varying Risk Premiums," CREATES Research Papers 2012-35, Department of Economics and Business Economics, Aarhus University.
    6. Zbynek Stork, 2016. "Term Structure of Interest Rates: Macro-Finance Approach," EcoMod2016 9566, EcoMod.

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    Keywords

    Interest rates;

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