Short run and long run causality in time series: inference |
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| Authors: | Jean-Marie Dufour Denis Pelletier Éric Renault |
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| Institution: | 1. CIRANO, CIREQ, and Département de sciences économiques, Université de Montréal, C.P. 6128 succursale Centre-ville, Montréal, Qué., Canada H3C 3J7;2. CIRANO, CIREQ, Université de Montréal, and Department of Economics, North Carolina State University, Campus Box 8110, Raleigh, NC 27695-8110, USA;3. CIRANO, CIREQ, and Département de sciences économiques, Université de Montréal, C.P. 6128 succursale Centre-ville, Montréal, Qué., Canada H3C 3J7 |
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| Abstract: | We propose methods for testing hypothesis of non-causality at various horizons, as defined in Dufour and Renault (Econometrica 66, (1998) 1099–1125). 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 US economy. |
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| Keywords: | C1 C12 C15 C32 C51 C53 E3 E4 E52 |
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