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Unbiased estimate of dynamic term structure models

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

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.

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Paper provided by Federal Reserve Bank of San Francisco in its series Working Paper Series with number 2011-12.

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Date of creation: 2011
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Handle: RePEc:fip:fedfwp:2011-12
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  1. Mackinnon, J.G. & Smith, A.A., 1996. "Approximate Bias Correction in Econometrics," G.R.E.Q.A.M. 96a14, Universite Aix-Marseille III.
  2. Hanno Lustig & Nikolai Roussanov & Adrien Verdelhan, 2010. "Countercyclical Currency Risk Premia," NBER Working Papers 16427, National Bureau of Economic Research, Inc.
  3. Jens H. E. Christensen & Francis X. Diebold & Glenn D. Rudebusch, 2008. "An Arbitrage-Free Generalized Nelson-Siegel Term Structure Model," PIER Working Paper Archive 08-030, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  4. Jens H. E. Christensen & Francis X. Diebold & Glenn D. Rudebusch, 2007. "The Affine Arbitrage-Free Class of Nelson-Siegel Term Structure Models," PIER Working Paper Archive 07-029, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  5. Luis Gil-Alana & Antonio Moreno, . "Uncovering the U.S. Term Premium: An Alternative Route," Faculty Working Papers 12/07, School of Economics and Business Administration, University of Navarra.
  6. Glenn D. Rudebusch, 1990. "Trends and random walks in macroeconomic time series: a re-examination," Working Paper Series / Economic Activity Section 105, Board of Governors of the Federal Reserve System (U.S.).
  7. Jardet, C. & Monfort, A. & Pegoraro, F., 2009. "No-arbitrage Near-Cointegrated VAR(p) Term Structure Models, Term Premia and GDP Growth," Working papers 234, Banque de France.
  8. 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.
  9. Amihud, Yakov & Hurvich, Clifford M., 2004. "Predictive Regressions: A Reduced-Bias Estimation Method," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 39(04), pages 813-841, December.
  10. Ang, Andrew & Piazzesi, Monika, 2003. "A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables," Journal of Monetary Economics, Elsevier, vol. 50(4), pages 745-787, May.
  11. Andrew Ang & Jean Boivin & Sen Dong & Rudy Loo-Kung, 2011. "Monetary Policy Shifts and the Term Structure," Review of Economic Studies, Oxford University Press, vol. 78(2), pages 429-457.
  12. Gregory R. Duffee, 2002. "Term Premia and Interest Rate Forecasts in Affine Models," Journal of Finance, American Finance Association, vol. 57(1), pages 405-443, 02.
  13. Michael D. Bauer, 2011. "Term premia and the news," Working Paper Series 2011-03, Federal Reserve Bank of San Francisco.
  14. 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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