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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.

Suggested Citation

  • DUFOUR, Jean-Marie & PELLETIER, Denis & RENAULT, Éric, 2003. "Short run and long run causality in time series: Inference," Cahiers de recherche 2003-16, Universite de Montreal, Departement de sciences economiques.
    • Handle: RePEc:mtl:montde:2003-16
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    More about this item

    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;
    All these keywords.

    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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