  We propose a new class of goodness-of-fit tests for the inverse Gaussian
distribution. The proposed tests are weighted $L^2$-type tests depending on a
tuning parameter. We develop the asymptotic theory under the null hypothesis
and under a broad class of alternative distributions. These results are used to
show that the parametric bootstrap procedure, which we employ to implement the
test, is asymptotically valid and that the whole test procedure is consistent.
A comparative simulation study for finite sample sizes shows that the new
procedure is competitive to classical and recent tests, outperforming these
other methods almost uniformly over a large set of alternative distributions.
The use of the newly proposed test is illustrated with two observed data sets.
