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Persistence-robust Granger causality testing

Author

Listed:
  • Dietmar Bauer

    (Arsenal Research, Vienna, Austria)

  • Alex Maynard

    (Department of Economics, University of Guelph, Canada.)

Abstract

The observed persistence common in economic time series may arise from a variety of models that are not always distinguished with confidence in practice, yet play an important role in model specification and second stage inference procedures. Previous literature has introduced causality tests with conventional limiting distributions in I(0)/I(1)VAR models with unknown integration orders, based on an additional surplus lag in the specification of the estimated equation, which is not included in the tests. Building on this approach, but using an infinite order VARX framework, we provide a highly persistence-robust Granger causality test that accommodates i.a. stationary, nonstationary, local-to-unity, long-memory, and certain (unmodelled) structural break processes in the forcing variables within the context of a single Chi-Squared null limiting distribution. No first stage testing or estimation is required and known lag orders are not assumed.

Suggested Citation

  • Dietmar Bauer & Alex Maynard, 2010. "Persistence-robust Granger causality testing," Working Papers 1011, University of Guelph, Department of Economics and Finance.
  • Handle: RePEc:gue:guelph:2010-11.
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    File URL: http://www.uoguelph.ca/economics/repec/workingpapers/2010/2010-11.pdf
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    References listed on IDEAS

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    Citations

    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Surplus-Lag Granger Causality Testing
      by Dave Giles in Econometrics Beat: Dave Giles' Blog on 2012-04-18 21:13:00
    2. Surplus-Lag Granger Causality Testing
      by Dave Giles in Econometrics Beat: Dave Giles' Blog on 2012-04-18 21:13:00

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    More about this item

    Keywords

    Granger causality; surplus lag; nonstationary; VAR; local-to-unity; long-memory;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: 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

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