  We propose a new class of goodness-of-fit tests for the logistic distribution
based on a characterisation related to the density approach in the context of
Stein's method. This characterisation based test is a first of its kind for the
logistic distribution. The asymptotic null distribution of the test statistic
is derived and it is shown that the test is consistent against fixed
alternatives. The finite sample power performance of the newly proposed class
of tests is compared to various existing tests by means of a Monte Carlo study.
It is found that this new class of tests are especially powerful when the
alternative distributions are heavy tailed, like Student's t and Cauchy, or for
skew alternatives such as the log-normal, gamma and chi-square distributions.
