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Dynamic Factor Models with Smooth Loadings for Analyzing the Term Structure of Interest Rates

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Author Info
Borus Jungbacker () (Department of Econometrics, VU University Amsterdam)
Siem Jan Koopman () (Department of Econometrics, VU University Amsterdam and Tinbergen Institute)
Michel van der Wel () (Tinbergen Institute + Erasmus School of Economics, ERIM Rotterdam + CREATES, Aarhus University)

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Abstract

We propose a new approach to the modelling of the term structure of interest rates. We consider the general dynamic factor model and show how to impose smoothness restrictions on the factor loadings. We further present a statistical procedure based on Wald tests that can be used to find a suitable set of such restrictions. We present these developments in the context of term structure models, but they are also applicable in other settings. We perform an empirical study using a data set of unsmoothed Fama- Bliss zero yields for US treasuries of different maturities. The general dynamic factor model with and without smooth loadings is considered in this study together with models that are associated with Nelson-Siegel and arbitrage-free frameworks. These existing models can be regarded as special cases of the dynamic factor model with restrictions on the model parameters. For all model candidates, we consider both stationary and nonstationary autoregressive processes (with different numbers of lags) for the latent factors. Finally, we perform statistical hypothesis tests to verify whether the restrictions imposed by the models are supported by the data. Our main conclusion is that smoothness restrictions can be imposed on the loadings of dynamic factor models for the term structure of US interest rates but that the restrictions implied by a number of popular term structure models are rejected.

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Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2009-39.

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Length: 52
Date of creation: 08 Sep 2009
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Handle: RePEc:aah:create:2009-39

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Related research
Keywords: Fama-Bliss data set; Kalman filter; Maximum likelihood; Yield curve;

Other versions of this item:

Find related papers by JEL classification:
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions
C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Determination of Interest Rates; Term Structure of Interest Rates

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  1. Jens H. E. Christensen & Francis X. Diebold & Glenn D. Rudebusch, 2007. "The Affine Arbitrage-Free Class of: Nelson-Siegel Term Structure Models," NBER Working Papers 13611, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  2. Robert R. Bliss, 1997. "Movements in the term structure of interest rates," Economic Review, Federal Reserve Bank of Atlanta, issue Q 4, pages 16-33. [Downloadable!]
  3. Diebold, Francis X. & Li, Canlin, 2006. "Forecasting the term structure of government bond yields," Journal of Econometrics, Elsevier, vol. 130(2), pages 337-364, February. [Downloadable!] (restricted)
    Other versions:
  4. Cox, John C & Ingersoll, Jonathan E, Jr & Ross, Stephen A, 1985. "A Theory of the Term Structure of Interest Rates," Econometrica, Econometric Society, vol. 53(2), pages 385-407, March. [Downloadable!] (restricted)
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  6. Gregory, Allan W & Head, Allen C & Raynauld, Jacques, 1997. "Measuring World Business Cycles," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 38(3), pages 677-701, August.
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  7. Koopman, S.J. & Durbin, J., 1998. "Fast filtering and smoothing for multivariate state space models," Discussion Paper 18, Tilburg University, Center for Economic Research. [Downloadable!]
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  10. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  11. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January. [Downloadable!] (restricted)
  12. Hall, Anthony D & Anderson, Heather M & Granger, Clive W J, 1992. "A Cointegration Analysis of Treasury Bill Yields," The Review of Economics and Statistics, MIT Press, vol. 74(1), pages 116-26, February. [Downloadable!] (restricted)
  13. Bowsher, Clive G. & Meeks, Roland, 2008. "The Dynamics of Economic Functions: Modeling and Forecasting the Yield Curve," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1419-1437. [Downloadable!] (restricted)
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  14. Engle, Robert F., 1984. "Wald, likelihood ratio, and Lagrange multiplier tests in econometrics," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 13, pages 775-826 Elsevier. [Downloadable!] (restricted)
  15. Connor, Gregory & Korajczyk, Robert A, 1993. " A Test for the Number of Factors in an Approximate Factor Model," Journal of Finance, American Finance Association, vol. 48(4), pages 1263-91, September. [Downloadable!] (restricted)
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