A bootstrap goodness of fit test for the generalized Pareto distribution

This paper proposes a bootstrap goodness of fit test for the Generalized Pareto distribution (GPd) with shape parameter γγ. The proposed test is an intersection–union test which tests separately the cases of γ≥0γ≥0 and γ<0γ<0 and rejects if both cases are rejected. If the test does not reject, then it is known whether the shape parameter γγ is either positive or negative. A Monte Carlo simulation experiment was conducted to assess the power of performance of the intersection–union test. The GPd hypothesis was tested on a data set containing Mexico City’s ozone levels. 1