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Least Squares Learning and the US Treasury Bill Rate

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  • Sagarika Mishra

    ()

  • Sandeep Dhole

Abstract

Understanding how agents formulate their expectations about Fed behavior is important for market participants because they can potentially use this information to make more accurate estimates of stock and bond prices. Although it is commonly assumed that agents learn over time, there is scant empirical evidence in support of this assumption. Thus, in this paper we test if the forecast of the three month T-bill rate in the Survey of Professional Forecasters (SPF) is consistent with least squares learning when there are discrete shifts in monetary policy. We first derive the mean, variance and autocovariances of the forecast errors from a recursive least squares learning algorithm when there are breaks in the structure of the model. We then apply the Bai and Perrron (1998) test for structural change to a forecasting model for the three month T-bill rate in order to identify changes in monetary policy. Having identified the policy regimes, we then estimate the implied biases in the interest rate forecasts within each regime. We find that when the forecast errors from the SPF are corrected for the biases due to shifts in policy, the forecasts are consistent with least squares learning.

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File URL: http://www.deakin.edu.au/buslaw/aef/workingpapers/fin-econometrics/2013_05.pdf
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Bibliographic Info

Paper provided by Deakin University, Faculty of Business and Law, School of Accounting, Economics and Finance in its series Financial Econometics Series with number 2013_05.

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Handle: RePEc:dkn:ecomet:fe_2013_05

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Related research

Keywords: Survey forecasts; Least Squares Learning;

References

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  1. Perron, P. & Bai, J., 1995. "Estimating and Testing Linear Models with Multiple Structural Changes," Cahiers de recherche 9552, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  2. Zarnowitz, Victor, 1985. "Rational Expectations and Macroeconomic Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 3(4), pages 293-311, October.
  3. Branch, William A. & Evans, George W., 2006. "A simple recursive forecasting model," Economics Letters, Elsevier, vol. 91(2), pages 158-166, May.
  4. Evans, George W & Honkapohja, Seppo, 2002. "Monetary Policy, Expectations and Commitment," CEPR Discussion Papers 3434, C.E.P.R. Discussion Papers.
  5. Goyenko, Ruslan & Subrahmanyam, Avanidhar & Ukhov, Andrey, 2011. "The Term Structure of Bond Market Liquidity and Its Implications for Expected Bond Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 46(01), pages 111-139, March.
  6. Shiu-Sheng Chen, 2005. "Does Monetary Policy Have Asymmetric Effects on Stock Returns?," Macroeconomics 0502001, EconWPA, revised 01 Feb 2005.
  7. Paresh Kumar Narayan & Stephan Popp, 2009. "A New Unit Root Test with Two Structural Breaks in Level and Slope at Unknown Time," Economics Series 2009_11, Deakin University, Faculty of Business and Law, School of Accounting, Economics and Finance.
  8. John Duffy and Jim Engle-Warnick, 2001. "Multiple Regimes in U.S. Monetary Policy? A Nonparametric Approach," Computing in Economics and Finance 2001 151, Society for Computational Economics.
  9. Balduzzi, Pierluigi & Elton, Edwin J. & Green, T. Clifton, 2001. "Economic News and Bond Prices: Evidence from the U.S. Treasury Market," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 36(04), pages 523-543, December.
  10. Richard Clarida & Jordi Galí & Mark Gertler, 1997. "Monetary policy rules and macroeconomic stability: Evidence and some theory," Economics Working Papers 350, Department of Economics and Business, Universitat Pompeu Fabra, revised May 1999.
  11. Tarun Chordia, 2005. "An Empirical Analysis of Stock and Bond Market Liquidity," Review of Financial Studies, Society for Financial Studies, vol. 18(1), pages 85-129.
  12. Rudebusch, G.D., 1996. "Do Measures of Monetary Policy in a VAR Make Sense?," Papers 269, Banca Italia - Servizio di Studi.
  13. Fabio Milani, 2005. "Expectations, Learning and Macroeconomic Persistence," Working Papers 050608, University of California-Irvine, Department of Economics.
  14. Caskey, John P, 1985. "Modeling the Formation of Price Expectations: A Bayesian Approach," American Economic Review, American Economic Association, vol. 75(4), pages 768-76, September.
  15. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
  16. Fischer, Stanley, 1977. "Long-Term Contracts, Rational Expectations, and the Optimal Money Supply Rule," Journal of Political Economy, University of Chicago Press, vol. 85(1), pages 191-205, February.
  17. Preston, Bruce, 2006. "Adaptive learning, forecast-based instrument rules and monetary policy," Journal of Monetary Economics, Elsevier, vol. 53(3), pages 507-535, April.
  18. Caporale, Tony & Grier, Kevin B, 2000. "Political Regime Change and the Real Interest Rate," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 32(3), pages 320-34, August.
  19. Thomas Urich & Paul Wachtel, 1983. "The Structure of Expectations of the Weekly Money Supply Announcement," NBER Working Papers 1090, National Bureau of Economic Research, Inc.
  20. Dean Croushore, 1993. "Introducing: the survey of professional forecasters," Business Review, Federal Reserve Bank of Philadelphia, issue Nov, pages 3-15.
  21. Rudebusch, Glenn D, 1998. "Do Measures of Monetary Policy in a VAR Make Sense? A Reply," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 943-48, November.
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