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Testing for Granger causality in panel data

Author

Listed:
  • Luciano Lopez

    (University of Neuchatel)

  • Sylvain Weber

    (University of Neuchatel)

Abstract

With the development of large and long panel databases, the theory surrounding panel causality evolves quickly, and empirical researchers might find it difficult to run the most recent techniques developed in the literature. In this article, we present the community-contributed command xtgcause, which imple- ments a procedure proposed by Dumitrescu and Hurlin (2012, Economic Modelling 29: 1450–1460) for detecting Granger causality in panel datasets. Thus, it con- stitutes an effort to help practitioners understand and apply the test. xtgcause offers the possibility of selecting the number of lags to include in the model by minimizing the Akaike information criterion, Bayesian information criterion, or Hannan–Quinn information criterion, and it offers the possibility to implement a bootstrap procedure to compute p-values and critical values.

Suggested Citation

  • Luciano Lopez & Sylvain Weber, 2017. "Testing for Granger causality in panel data," Stata Journal, StataCorp LP, vol. 17(4), pages 972-984, December.
    • Handle: RePEc:tsj:stataj:v:17:y:2017:i:4:p:972-984
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    Keywords

    xtgcause; Granger causality; panel datasets; bootstrap;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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