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Granger-causality in Markov Switching Models

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Author Info

  • Monica Billio

    ()
    (Department of Economics, University Of Venice Ca’ Foscari)

  • Silvestro Di Sanzo

    (Departamento de Fundamentos del Analisis Economico, Universidad de Alicante)

Abstract

In this paper we propose a new parametrisation of transition probabilities that allows us to characterize and test Granger-causality in Markov switching models by means of an appropriate specification of the transition matrix. Test for independence are also provided. We illustrate our methodology with an empirical application. In particular, we investigate the causality and interdependence between financial and economic cycles using a bivariate Markov switching model. When applied to U.S. data, we find that financial variables are useful for forecasting the direction of aggregate economic activity, and vice versa.

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File URL: http://www.unive.it/media/allegato/DIP/Economia/Working_papers/Working_papers_2006/WP_DSE_Billio_Di_Sanzo_20_06.pdf
File Function: First version, 2006
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Bibliographic Info

Paper provided by Department of Economics, University of Venice "Ca' Foscari" in its series Working Papers with number 2006_20.

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Length: 20 pages
Date of creation: 2006
Date of revision:
Handle: RePEc:ven:wpaper:2006_20

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Related research

Keywords: Granger Causality; Markov Chains; Switching Models;

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References

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  1. Psaradakis, Zacharias & Ravn, Morten O. & Sola, Martin, 2003. "Markov Switching Causality and the Money-Output Relationship," CEPR Discussion Papers, C.E.P.R. Discussion Papers 3803, C.E.P.R. Discussion Papers.
  2. Perez-Quiros, G. & Timmermann, A., 2001. "Business Cycle Asymmetries in Stock Returns: Evidence from Higher Order Moments and Conditional Densities," Papers 58, Quebec a Montreal - Recherche en gestion.
  3. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, The MIT Press, edition 1, volume 1, number 0262112388, December.
  4. Mosconi, Rocco & Seri, Raffaello, 2006. "Non-causality in bivariate binary time series," Journal of Econometrics, Elsevier, Elsevier, vol. 132(2), pages 379-407, June.
  5. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, Econometric Society, vol. 37(3), pages 424-38, July.
  6. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, Econometric Society, vol. 57(2), pages 357-84, March.
  7. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, Econometric Society, vol. 59(2), pages 347-70, March.
  8. Hamilton, James D & Gang, Lin, 1996. "Stock Market Volatility and the Business Cycle," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 11(5), pages 573-93, Sept.-Oct.
  9. Sims, Christopher A & Stock, James H & Watson, Mark W, 1990. "Inference in Linear Time Series Models with Some Unit Roots," Econometrica, Econometric Society, Econometric Society, vol. 58(1), pages 113-44, January.
  10. Billio, Monica & Pelizzon, Loriana, 2003. "Volatility and shocks spillover before and after EMU in European stock markets," Journal of Multinational Financial Management, Elsevier, Elsevier, vol. 13(4-5), pages 323-340, December.
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Cited by:
  1. Chevallier, Julien, 2012. "Global imbalances, cross-market linkages, and the financial crisis: A multivariate Markov-switching analysis," Economic Modelling, Elsevier, Elsevier, vol. 29(3), pages 943-973.

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