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Short run and long run causality in time series: inference

  • Dufour, Jean-Marie
  • Pelletier, Denis
  • Renault, Eric

We propose methods for testing hypotheses of non-causality at various horizons, as defined in Dufour and Renault (1998, Econometrica). We study in detail the case of VAR models and we propose linear methods based on running vector autoregressions at different horizons. While the hypotheses considered are nonlinear, the proposed methods only require linear regression techniques as well as standard Gaussian asymptotic distributional theory. Bootstrap procedures are also considered. For the case of integrated processes, we propose extended regression methods that avoid nonstandard asymptotics. The methods are applied to a VAR model of the U.S. economy.

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Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 132 (2006)
Issue (Month): 2 (June)
Pages: 337-362

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Handle: RePEc:eee:econom:v:132:y:2006:i:2:p:337-362
Contact details of provider: Web page: http://www.elsevier.com/locate/jeconom

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  1. Dufour, Jean-Marie & Pelletier, Denis & Renault, Eric, 2006. "Short run and long run causality in time series: inference," Journal of Econometrics, Elsevier, vol. 132(2), pages 337-362, June.
  2. Yamada, Hiroshi & Toda, Hiro Y., 1998. "Inference in possibly integrated vector autoregressive models: some finite sample evidence," Journal of Econometrics, Elsevier, vol. 86(1), pages 55-95, June.
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