Short run and long run causality in time series: Inference

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

Cahiers de recherche from Universite de Montreal, Departement de sciences economiques

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; multie horizon causality; autoregression; autoregressive model; vector autoregression; VAR; stationary ocess; nonstationary ocess; integrated ocess; unit root; extended autoregression; bootstra Monte Carlo; macroeconomics; money; interest rates; outt; inflation (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-rmg
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:montde:2003-16

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