Extreme Value Modelling in Stata

David Roodman wrote to me today, saying:

"I don’t know if you use Stata, but I’ve just released a Stata package for extreme value theory. It is strongly influenced by Coles’s book on EVT and the associated ismev package for R. Using maximum likelihood, it fits the generalized Pareto distribution and the generalized extreme value distribution, the latter including the extension to multiple order statistics. It also offers various diagnostic plots. There are already many sophisticated R packages for EVT. I suppose mine offers accessibility…and small-sample bias corrections. It can do the Cox-Snell correction for all the models, including with covariates (citing you for GPD, and promising a write-up for the rest). It also offers bias correction based on a parametric bootstrap. I’ve confirmed the efficacy of both bias corrections through simulations, for the GPD and GEV. I’m still tweaking the simulations, and they take time, but I hope to soon post some graphs based on them. The GPD results closely match yours.

Comments welcome. Please circulate to others who might be interested. To install the package in Stata, type “ssc install extreme”.  The help file contains clickable examples that reproduce most results in the Coles book. The web page is https://ideas.repec.org/c/boc/bocode/s457953.html."

The work on the Cox-Snell bias correction for the Generalized Pareto Distribution that David is referring to is Giles et al. (2015). You can find an earlier post about this work here, and you can download the paper here.

Update, 28/1/2015: See more at David's blog.

Reference

Giles, D. E., H. Feng, and R. T. Godwin, 2015. Bias-corrected maximum likelihood estimation of the parameters of the generalized Pareto distribution. Communications in Statistics - Theory and Methods, in press.

© 2015, David E. Giles