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Contemporaneous Threshold Autoregressive Models: Estimation, Testing and Forecasting

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  • Michael Dueker
  • Martin Sola
  • Fabio Spagnolo

Abstract

This paper proposes a contemporaneous smooth transition threshold autoregressive model (C-STAR) as a modification of the smooth transition threshold autoregressive model surveyed in Teräsvirta (1998), in which the regime weights depend on the ex ante probability that a latent regime-specific variable will exceed a threshold value. We argue that the contemporaneous model is well-suited to rational expectations applications (and pricing exercises), in that it does not require the initial regimes to be predetermined. We investigate the properties of the model and evaluate its finitesample maximum likelihood performance. We also propose a method to determine the number of regimes based on a modified Hansen (1992) procedure. Furthermore, we construct multiple-step ahead forecasts and evaluate the forecasting performance of the model. Finally, an empirical application of the short term interest rate yield is presented and discussed.

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Bibliographic Info

Paper provided by D.E.S. (Department of Economic Studies), University of Naples "Parthenope", Italy in its series Discussion Papers with number 5_2007.

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Date of creation: 01 Apr 2007
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Handle: RePEc:prt:dpaper:5_2007

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Keywords: Smooth Transition Threshold Autoregressive; Forecasting; Nonlinear Models;

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Citations

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Cited by:
  1. repec:wyi:journl:002152 is not listed on IDEAS
  2. Dueker Michael J. & Psaradakis Zacharias & Sola Martin & Spagnolo Fabio, 2011. "Contemporaneous-Threshold Smooth Transition GARCH Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(2), pages 1-25, March.
  3. Chen, Haiqiang & Chong, Terence Tai Leung & Bai, Jushan, 2012. "Theory and Applications of TAR Model with Two Threshold Variables," MPRA Paper 54527, University Library of Munich, Germany.
  4. Dueker, Michael J. & Sola, Martin & Spagnolo, Fabio, 2007. "Contemporaneous threshold autoregressive models: Estimation, testing and forecasting," Journal of Econometrics, Elsevier, vol. 141(2), pages 517-547, December.
  5. Emilio Zanetti Chini, 2013. "Generalizing smooth transition autoregressions," CREATES Research Papers 2013-32, School of Economics and Management, University of Aarhus.
  6. Michael J. Dueker & Zacharias Psaradakis & Martin Sola & Fabio Spagnolo, 2007. "Multivariate contemporaneous threshold autoregressive models," Working Papers 2007-019, Federal Reserve Bank of St. Louis.
  7. Paulo M.M. Rodrigues & Nazarii Salish, 2011. "Modeling and Forecasting Interval Time Series with Threshold Models: An Application to S&P500 Index Returns," Working Papers w201128, Banco de Portugal, Economics and Research Department.
  8. Francesco Battaglia & Mattheos Protopapas, 2012. "An analysis of global warming in the Alpine region based on nonlinear nonstationary time series models," Statistical Methods and Applications, Springer, vol. 21(3), pages 315-334, August.
  9. Francesco Battaglia & Mattheos Protopapas, 2012. "Multi–regime models for nonlinear nonstationary time series," Computational Statistics, Springer, vol. 27(2), pages 319-341, June.
  10. Bel, K. & Paap, R., 2013. "Modeling the impact of forecast-based regime switches on macroeconomic time series," Econometric Institute Research Papers EI 2013-25, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

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