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Multivariate Contemporaneous Threshold Autoregressive Models

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Author Info
Michael Dueker
Zacharias Psaradakis
Martin Sola ()
Fabio Spagnolo

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Abstract

In this paper we propose a contemporaneous threshold multivariate smooth transition autoregressive (C-MSTAR) model in which the regime weights depend on the ex ante probabilities that latent regime-specific variables exceed certain threshold values. A key feature of the model is that the transition function depends on all the parameters of the model as well as on the data. Since the mixing weights are a function of the regime-specific contemporaneous variance-covariance matrix, the model can account for contemporaneous regime-specific co-movements of the variables. The stability and distributional properties of the proposed model are discussed, as well as issues of estimation, testing and forecasting. The practical usefulness of the C-MSTAR model is illustrated by examining the relationship between US stock prices and interest rates and discussing the regime specific Granger causality relationships.

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Publisher Info
Paper provided by Universidad Torcuato Di Tella in its series Department of Economics Working Papers with number 2009-03.

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Length: 52 pages
Date of creation: Mar 2009
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Handle: RePEc:udt:wpecon:2009-03

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Related research
Keywords: Nonlinear autoregressive models; Smooth transition; Stability; Threshold.;

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Find related papers by JEL classification:
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions
G12 - Financial Economics - - General Financial Markets - - - Asset Pricing

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  1. 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. [Downloadable!] (restricted)
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  2. Sola, Martin & Driffill, John, 1994. "Testing the term structure of interest rates using a stationary vector autoregression with regime switching," Journal of Economic Dynamics and Control, Elsevier, vol. 18(3-4), pages 601-628. [Downloadable!] (restricted)
  3. De Gooijer, Jan G. & Vidiella-i-Anguera, Antoni, 2004. "Forecasting threshold cointegrated systems," International Journal of Forecasting, Elsevier, vol. 20(2), pages 237-253. [Downloadable!] (restricted)
  4. Ben S. Bernanke & Mark Gertler, 2001. "Should Central Banks Respond to Movements in Asset Prices?," American Economic Review, American Economic Association, vol. 91(2), pages 253-257, May. [Downloadable!] (restricted)
  5. Hansen, Bruce E, 1992. "The Likelihood Ratio Test under Nonstandard Conditions: Testing the Markov Switching Model of GNP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages S61-82, Suppl. De. [Downloadable!] (restricted)
  6. Morten O. Ravn & Zacharias Psaradakis & Martin Sola, 2005. "Markov switching causality and the money-output relationship," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(5), pages 665-683. [Downloadable!]
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  7. Dick van Dijk & Timo Teräsvirta & Philip Hans Franses, 2002. "Smooth Transition Autoregressive Models - A Survey Of Recent Developments," Econometric Reviews, Taylor and Francis Journals, vol. 21(1), pages 1-47. [Downloadable!] (restricted)
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  8. Frédérique Bec & Anders Rahbek & Neil Shephard, 2008. "The ACR model: a multivariate dynamic mixture autoregression," THEMA Working Papers 2008-11, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise. [Downloadable!]
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  9. Zacharias Psaradakis & Martin Sola & Fabio Spagnolo & Nicola Spagnolo, 2009. "Selecting nonlinear time series models using information criteria," Journal of Time Series Analysis, Blackwell Publishing, vol. 30(4), pages 369-394, 07. [Downloadable!] (restricted)
  10. Zacharias Psaradakis & Nicola Spagnolo, 2006. "Joint Determination of the State Dimension and Autoregressive Order for Models with Markov Regime Switching," Journal of Time Series Analysis, Blackwell Publishing, vol. 27(5), pages 753-766, 09. [Downloadable!] (restricted)
  11. Harvill, Jane L. & Ray, Bonnie K., 2006. "Functional coefficient autoregressive models for vector time series," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3547-3566, August. [Downloadable!] (restricted)
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