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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. George W. Evans & Seppo Honkapohja, 2002. "Monetary Policy, Expectations and Commitment," University of Oregon Economics Department Working Papers 2002-11, University of Oregon Economics Department, revised 01 Feb 2004.
  2. Milani, Fabio, 2007. "Expectations, learning and macroeconomic persistence," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 2065-2082, October.
  3. Paresh Kumar Narayan & Stephan Popp, 2010. "A new unit root test with two structural breaks in level and slope at unknown time," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(9), pages 1425-1438.
  4. 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.
  5. Glenn D. Rudebusch, 1996. "Do measures of monetary policy in a VAR make sense?," Working Papers in Applied Economic Theory 96-05, Federal Reserve Bank of San Francisco.
  6. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
  7. Duffy, John & Engle-Warnick, Jim, 2006. "Multiple Regimes in U.S. Monetary Policy? A Nonparametric Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1363-1377, August.
  8. 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.
  9. Shiu-Sheng Chen, 2007. "Does Monetary Policy Have Asymmetric Effects on Stock Returns?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(2-3), pages 667-688, 03.
  10. 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.
  11. 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.
  12. Branch, William A. & Evans, George W., 2006. "A simple recursive forecasting model," Economics Letters, Elsevier, vol. 91(2), pages 158-166, May.
  13. Dean Croushore, 1993. "Introducing: the survey of professional forecasters," Business Review, Federal Reserve Bank of Philadelphia, issue Nov, pages 3-15.
  14. 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.
  15. Richard Clarida & Jordi Galí & Mark Gertler, 2000. "Monetary Policy Rules And Macroeconomic Stability: Evidence And Some Theory," The Quarterly Journal of Economics, MIT Press, vol. 115(1), pages 147-180, February.
  16. Zarnowitz, Victor, 1985. "Rational Expectations and Macroeconomic Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 3(4), pages 293-311, October.
  17. 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.
  18. 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.
  19. 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.
  20. Preston, Bruce, 2006. "Adaptive learning, forecast-based instrument rules and monetary policy," Journal of Monetary Economics, Elsevier, vol. 53(3), pages 507-535, April.
  21. 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.
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