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 implements a procedure proposed by
Dumitrescu and Hurlin (2012, Economic Modelling 29: 1450–1460)
for detecting Granger causality in panel datasets. Thus, it constitutes 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.
