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Short and long run causality measures: Theory and inference

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  • Dufour, Jean-Marie
  • Taamouti, Abderrahim

Abstract

The concept of causality introduced by Wiener [Wiener, N., 1956. The theory of prediction, In: E.F. Beckenback, ed., The Theory of Prediction, McGraw-Hill, New York (Chapter 8)] and Granger [Granger, C. W.J., 1969. Investigating causal relations by econometric models and cross-spectral methods, Econometrica 37, 424-459] is defined in terms of predictability one period ahead. This concept can be generalized by considering causality at any given horizon h as well as tests for the corresponding non-causality [Dufour, J.-M., Renault, E., 1998. Short-run and long-run causality in time series: Theory. Econometrica 66, 1099-1125; Dufour, J.-M., Pelletier, D., Renault, É., 2006. Short run and long run causality in time series: Inference, Journal of Econometrics 132 (2), 337-362]. Instead of tests for non-causality at a given horizon, we study the problem of measuring causality between two vector processes. Existing causality measures have been defined only for the horizon 1, and they fail to capture indirect causality. We propose generalizations to any horizon h of the measures introduced by Geweke [Geweke, J., 1982. Measurement of linear dependence and feedback between multiple time series. Journal of the American Statistical Association 77, 304-313]. Nonparametric and parametric measures of unidirectional causality and instantaneous effects are considered. On noting that the causality measures typically involve complex functions of model parameters in VAR and VARMA models, we propose a simple simulation-based method to evaluate these measures for any VARMA model. We also describe asymptotically valid nonparametric confidence intervals, based on a bootstrap technique. Finally, the proposed measures are applied to study causality relations at different horizons between macroeconomic, monetary and financial variables in the US.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 154 (2010)
Issue (Month): 1 (January)
Pages: 42-58

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Handle: RePEc:eee:econom:v:154:y:2010:i:1:p:42-58

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Web page: http://www.elsevier.com/locate/jeconom

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Keywords: Time series Granger causality Indirect causality Multiple horizon causality Causality measure Predictability Autoregressive model Vector autoregression VAR Bootstrap Monte Carlo Macroeconomics Money Interest rates Output Inflation;

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References

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Citations

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Cited by:
  1. Ren, Yunwen & Xiao, Zhiguo & Zhang, Xinsheng, 2013. "Two-step adaptive model selection for vector autoregressive processes," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 349-364.
  2. Hui Jun Zhang & Jean-Marie Dufour & John Galbraith, 2013. "Exchange rates and commodity prices: measuring causality at multiple horizons," CIRANO Working Papers 2013s-39, CIRANO.
  3. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
  4. Javier Ruiz-Castillo, 2012. "From the “European Paradox” to a European Drama in citation impact," Economics Working Papers we1211, Universidad Carlos III, Departamento de Economía.
  5. Majid M. Al-Sadoon, 2013. "Geometric and long run aspects of Granger causality," Economics Working Papers 1356, Department of Economics and Business, Universitat Pompeu Fabra.
  6. Matilla-García, Mariano & Marín, Manuel Ruiz & Dore, Mohammed I., 2014. "A permutation entropy based test for causality: The volume–stock price relation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 398(C), pages 280-288.
  7. Taamouti, Abderrahim & Bouezmarni, Taoufik & El Ghouch, Anouar, 2014. "Nonparametric estimation and inference for conditional density based Granger causality measures," Journal of Econometrics, Elsevier, vol. 180(2), pages 251-264.
  8. Abderrahim Taamouti & Taoufik Bouezmarni & Anouar El Ghouch, 2012. "Nonparametric estimation and inference for Granger causality measures," Economics Working Papers we1217, Universidad Carlos III, Departamento de Economía.
  9. Francis X. Diebold & Kamil Yilmaz, 2013. "Measuring the Dynamics of Global Business Cycle Connectedness," PIER Working Paper Archive 13-070, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  10. repec:cte:werepe:we1212 is not listed on IDEAS

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