Short Run and Long Run Causality in Time Series: Inference

Jean-Marie Dufour (), Denis Pelletier () and Éric RENAULT

Cahiers de recherche from Centre interuniversitaire de recherche en économie quantitative, CIREQ

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.

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 (search for similar items in EconPapers)
Date: 2003
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Related works:
Working Paper: Short run and long run causality in time series: Inference (2003)
Working Paper: Short Run and Long Run Causality in Time Series: Inference (2003)
Journal Article: Short run and long run causality in time series: inference (2006)
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Persistent link: http://EconPapers.repec.org/RePEc:mtl:montec:14-2003

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