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Short Run and Long Run Causality in Time Series : Inference

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  • DUFOUR, Jean-Marie
  • PELLETIER, Denis
  • RENAULT, Éric

Abstract

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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Bibliographic Info

Paper provided by Centre interuniversitaire de recherche en économie quantitative, CIREQ in its series Cahiers de recherche with number 14-2003.

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Length: 28 pages
Date of creation: 2003
Date of revision:
Handle: RePEc:mtl:montec:14-2003

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Keywords: Time series; Granger causality; indirect causality; multiple horizon causality; autoregression; autoregressive model; vector autoregression; VAR; stationary process; nonstationary process; integrated process; unit root; extended autoregression; bootstrap; Monte Carlo; macroeconomics; money; interest rates; output; inflation;

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References

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