Improved tests for Granger noncausality in panel data

Artūras Juodis, Yiannis Karavias, Vasilis Sarafidis, Jan Ditzen and Jiaqi Xiao
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Jiaqi Xiao: University of Birmingham

Swiss Stata Conference 2022 from Stata Users Group

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.

Date: 2022-11-30
New Economics Papers: this item is included in nep-eff
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Citations: View citations in EconPapers (7)

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http://repec.org/csug2022/Karavias-Bern2022-xtgranger.pdf

Related works:
Journal Article: Improved tests for Granger noncausality in panel data (2023)
Working Paper: Improved Tests for Granger Non-Causality in Panel Data (2022)
Working Paper: Improved Tests for Granger Non-Causality in Panel Data (2021)
Working Paper: Improved Tests for Granger Non-Causality in Panel Data (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:boc:csug22:06

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