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Modeling and Forecasting Short-term Interest Rates: The Benefits of Smooth Regimes, Macroeconomic Variables, and Bagging

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  • Francesco Audrino

    ()
    (University of St. Gallen)

  • Marcelo Cunha Medeiros

    ()
    (Department of Economics PUC-Rio)

Abstract

In this paper we propose a smooth transition tree model for both the conditional mean and variance of the short-term interest rate process. The estimation of such models is addressed and the asymptotic properties of the quasi-maximum likelihood estimator are derived. Model specification is also discussed. When the model is applied to the US short-term interest rate we find (1) leading indicators for inflation and real activity are the most relevant predictors in characterizing the multiple regimes’ structure; (2) the optimal model has three limiting regimes. Moreover, we provide empirical evidence of the power of the model in forecasting the first two conditional moments when it is used in connection with bootstrap aggregation (bagging).

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Bibliographic Info

Paper provided by Department of Economics PUC-Rio (Brazil) in its series Textos para discussão with number 570.

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Length: 39p
Date of creation: Mar 2010
Date of revision:
Handle: RePEc:rio:texdis:570

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Keywords: short-term interest rate; regression tree; smooth transition; conditional variance; bagging; asymptotic theory;

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References

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  1. Medeiros, Marcelo C. & Teräsvirta, Timo & Rech, Gianluigi, 2002. "Building neural network models for time series: A statistical approach," Working Paper Series in Economics and Finance 508, Stockholm School of Economics.
  2. van Dijk, Dick & Teräsvirta, Timo & Franses, Philip Hans, 2000. "Smooth Transition Autoregressive Models - A Survey of Recent Developments," Working Paper Series in Economics and Finance 380, Stockholm School of Economics, revised 17 Jan 2001.
  3. Inoue, Atsushi & Kilian, Lutz, 2008. "How Useful Is Bagging in Forecasting Economic Time Series? A Case Study of U.S. Consumer Price Inflation," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 511-522, June.
  4. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
  5. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
  6. Audrino, Francesco, 2006. "Tree-Structured Multiple Regimes in Interest Rates," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 338-353, July.
  7. Andrew Ang & Sen Dong & Monika Piazzesi, 2005. "No-arbitrage Taylor rules," Proceedings, Federal Reserve Bank of San Francisco.
  8. White,Halbert, 1996. "Estimation, Inference and Specification Analysis," Cambridge Books, Cambridge University Press, number 9780521574464, April.
  9. Andrew Ang & Monika Piazzesi, 2001. "A No-Arbitrage Vector Autoregression of Term Structure Dynamics with Macroeconomic and Latent Variables," NBER Working Papers 8363, National Bureau of Economic Research, Inc.
  10. Francis X. Diebold & Glenn D. Rudebusch & S. Boragan Aruoba, 2004. "The Macroeconomy and the Yield Curve: A Dynamic Latent Factor Approach," NBER Working Papers 10616, National Bureau of Economic Research, Inc.
  11. Ravi Bansal & George Tauchen & Hao Zhou, 2004. "Regime Shifts, Risk Premiums in the Term Structure, and the Business Cycle," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 396-409, October.
  12. da Rosa, Joel Correa & Veiga, Alvaro & Medeiros, Marcelo C., 2008. "Tree-structured smooth transition regression models," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2469-2488, January.
  13. Eric Hillebrand & Marcelo Cunha Medeiros, 2007. "Forecasting realized volatility models:the benefits of bagging and nonlinear specifications," Textos para discussão 547, Department of Economics PUC-Rio (Brazil).
  14. Ravi Bansal & Hao Zhou, 2001. "Term structure of interest rates with regime shifts," Finance and Economics Discussion Series 2001-46, Board of Governors of the Federal Reserve System (U.S.).
  15. Wooldridge, Jeffrey M., 1990. "A Unified Approach to Robust, Regression-Based Specification Tests," Econometric Theory, Cambridge University Press, vol. 6(01), pages 17-43, March.
  16. Shiqing Ling & Michael McAleer, 2001. "Asymptotic Theory for a Vector ARMA-GARCH Model," ISER Discussion Paper 0549, Institute of Social and Economic Research, Osaka University.
  17. Francesco Audrino & Enrico De Giorgi, 0. "Beta Regimes for the Yield Curve," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 5(3), pages 456-490.
  18. Asbjørn T. Hansen & Rolf Poulsen, 2000. "A simple regime switching term structure model," Finance and Stochastics, Springer, vol. 4(4), pages 409-429.
  19. Medeiros, Marcelo C. & Veiga, Alvaro, 2009. "Modeling Multiple Regimes In Financial Volatility With A Flexible Coefficient Garch(1,1) Model," Econometric Theory, Cambridge University Press, vol. 25(01), pages 117-161, February.
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Citations

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Cited by:
  1. Huiyu Huang & Tae-Hwy Lee, 2013. "Forecasting Value-at-Risk Using High-Frequency Information," Econometrics, MDPI, Open Access Journal, vol. 1(1), pages 127-140, June.
  2. Erik Hillebrand & Tae-Hwy Lee & Marcelo Cunha Medeiros, 2012. "Let´s do it again: bagging equity premium predictors," Textos para discussão 604, Department of Economics PUC-Rio (Brazil).
  3. Francesco Audrino, 2012. "What Drives Short Rate Dynamics? A Functional Gradient Descent Approach," Computational Economics, Society for Computational Economics, vol. 39(3), pages 315-335, March.

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