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Improved tests for Granger noncausality in panel data

Author

Listed:
  • Arturas Juodis

    (University of Amsterdam)

  • Yiannis Karavias

    (University of Birmingham)

  • Vasilis Sarafidis

    (BI Norwegian Business School)

  • Jan Ditzen

    (Free University of Bozen-Bolzano)

  • Jiaqi Xiao

    (University of Birmingham)

Abstract

Granger causality is an important aspect of applied panel (longitudinal) data analysis because it can be used to determine whether one variable is useful in forecasting another. This presentation describes xtgranger, a community-contributed Stata command, which implements the panel Granger noncausality test of Juodis, Karavias, and Sarafidis (2021). This test offers superior size and power performance to existing tests, which stems from the use of a pooled estimator that has a faster convergence rate. The test has several other useful properties; it can be used in multivariate systems, it has power against both homogeneous as well as heterogeneous alternatives, and it allows for cross-section dependence and cross-section heteroskedasticity. The command is used to examine the type of temporal relation between profitability, cost efficiency, and asset quality in the U.S. banking industry.

Suggested Citation

  • Arturas Juodis & Yiannis Karavias & Vasilis Sarafidis & Jan Ditzen & Jiaqi Xiao, 2022. "Improved tests for Granger noncausality in panel data," Swiss Stata Conference 2022 06, Stata Users Group.
    • Handle: RePEc:boc:csug22:06
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    File URL: http://repec.org/csug2022/Karavias-Bern2022-xtgranger.pdf
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    JEL classification:

    • E50 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - General
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

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