Econometric Models with Regime-Changes

  • Prof. Dr. Michael Stein, Juniorprofessor für Finanzmarktökonometrie
Winter Semester 2015/2016
Di. 10-12 Uhr
R12 R06 A93
Lecture in Moodle
Linked Lectures:


Teaching of the relevant knowledge concerning econometric methods for modelling regime changes. Teaching of the theoretical fundamentals as well as in-depth discussion of the empirical studies. In the lecture the relevant theories, models and methods are presented and discussed. In the exercise class the knowledge about theories and methods introduced in the lecture is deepened and empirical studies are used to enhance the competencies in this area.

Learning Targets:


  • have an in-depth knowledge of the theories and models as well as the empirical research literature in the field of econometric modelling with regime dependencies.
  • have the required analytical abilities and understand the relevant methods in econometric models with regime changes.


  1. Introduction: Nonlinear Time Series Models
  2. (SE)TAR - (Self-Exciting) Threshold Autoregressive Models
  3. ST(A)R - Smooth Transition (Auto) Regressive Models
  4. MS – Markov-Switching Models
  5. Volatility Models with Regime-Changes
  6. Multivariate Models with Regime-Changes
  7. Multivariate Volatility Models with Regime-Changing Volatility
  8. Multivariate Volatility Models with Regime-Changing Correlation


  • Bauwens, L., A. Preminger and J.V.K. Rombouts (2010) Theory and inference for a Markov switching GARCH model. The Econometrics Journal, 13, pp. 218–244
  • Camacho, M. (2004) Vector Smooth Transition Regression Models for US GDP and the Composite Index of Leading Indicators. Journal of Forecasting, 23, pp. 173-196.
  • Chan, K.S. and Tong, H. (1986) On estimating thresholds in autoregressive models. Journal of Time Series Analysis, 7, pp. 179-190
  • Dueker, M.J. (1997) Markov switching in GARCH processes and mean-reverting stock-market volatility. Journal of Business & Economic Statistics, 15, pp. 26–34.
  • Dueker, M.J., Z. Psaradakis, M. Sola and F. Spagnolo (2011), Contemporaneous-Threshold Smooth Transition GARCH Models, Studies in Nonlinear Dynamics & Econometrics, 15(2).
  • Fan, J. and Yao, Q. (2003) Nonlinear Time Series: Nonparametric and Parametric Methods, Springer, New York.
  • Fok D., D. van Dijk and P. H. Franses (2005) A multi-level panel STAR model for US manufacturing sectors. Journal of Applied Econometrics, 20(6), pp. 811-827.
  • Franses, P.H., and D. van Dijk (2000). Non-Linear Time Series Models in Empirical Finance, Cambridge University Press, Cambridge.
  • González-Rivera, G. (1998) Smooth-Transition GARCH Models. Studies in Nonlinear Dynamics and Econometrics 3, pp. 61—78. Hamilton, J.D. (1989) A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), pp. 357-384.
  • Hamilton, J.D. (1990) Analysis of time series subject to changes in regime. Journal of Econometrics, 45, pp. 39-70
  • Hamilton, J.D. (2008) Regime switching models, in S.N. Durlauf and L.E. Blume (Eds.), The New Palgrave Dictionary of Economics, Palgrave Macmillan, Basingstoke
  • Hsieh, D.A. (1989) Testing for nonlinear dependence in daily forwards exchange rates. Journal of Business, 62, 339-368.
  • Lundbergh, S. and Teräsvirta, T. (1998) Modelling economic high-frequency time series with STAR-STGARCH Models, Working Paper Series in Economics and Finance, No. 291, Stockholm School of Economics
  • McCulloch, R.E. and Tsay, R.S. (1994) Statistical inference of macroeconomic time series via Markov switching models. Journal of the American Statistical Association, 88, pp. 968-978.
  • Pelletier, D. (2006). Regime switching for dynamic correlations, Journal of Econometrics, 131, pp. 445-473.
  • Silvennoinen, A. and Teräsvirta, T. (2013) Modelling conditional correlations of asset returns: A smooth transition approach. Econometric Reviews, forthcoming.
  • Teräsvirta, T. (2004). Smooth Transition Regression Modeling, in H. Lütkepohl and M. Krätzig (eds.), Applied Time Series Econometrics, Cambridge University Press, Cambridge.
  • Tong, H. (1978) On a threshold model, in C.H. Chen (ed.), Pattern Recognition and Signal Processing. Sijhoff & Noordhoff, Amsterdam.
  • Tong, H. (1983) Threshold Models in Nonlinear Time Series Analysis, Lecture Notes in Statistics, Springer, New York.
  • Tsay, R.S. (1998) Testing and modeling threshold autoregressive processes. Journal of the American Statistical Association, 84, pp. 231-240.
  • Tsay, R.S. (1998) Testing and modeling multivariate threshold models. Journal of the American Statistical Association, 93, pp. 1188-1202.
  • Tsay, R.S. (2010) Analysis of Financial Time Series. John Wiley & Sons, New Jersey.
  • Additional literature will be announced in the lecture.

Methods of Assessment:

Written examination (60 minutes, 70%), presentation of a relevant topic including a discussion (usually: 20 minutes, 20%) as well as the active participation in discussions of other presentations (10%).


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