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Short Run and Long Run Causality in Time Series: Inference

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  • Jean-Marie Dufour
  • Denis Pelletier
  • Éric Renault

Abstract

We propose methods for testing hypothesis 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. Nous proposons des méthodes pour tester des hypothèses de non-causalité à différents horizons, tel que défini dans Dufour et Renault (1998, Econometrica). Nous étudions le cas des modèles VAR en détail et nous proposons des méthodes linéaires basées sur l'estimation d'autorégressions vectorielles à différents horizons. Même si les hypothèses considérées sont non linéaires, les méthodes proposées ne requièrent que des techniques de régression linéaire de même que la théorie distributionnelle asymptotique gaussienne habituelle. Dans le cas des processus intégrés, nous proposons des méthodes de régression étendue qui ne requièrent pas de théorie asymptotique non standard. L'application du bootstrap est aussi considérée. Les méthodes sont appliquées à un modèle VAR de l'économie américaine.

Suggested Citation

  • Jean-Marie Dufour & Denis Pelletier & Éric Renault, 2003. "Short Run and Long Run Causality in Time Series: Inference," CIRANO Working Papers 2003s-61, CIRANO.
  • Handle: RePEc:cir:cirwor:2003s-61
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    More about this item

    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; séries chronologiques; causalité; causalité indirecte; causalité à différents horizons; autorégression; modèle autorégressif; autorégression vectorielle; VAR; processus stationnaire; processus non stationnaire; processus intégré; racine unitaire; autorégression étendue; bootstrap; Monte Carlo; macroéconomie; monnaie; taux d'intérêt; production; inflation;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • E4 - Macroeconomics and Monetary Economics - - Money and Interest Rates
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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