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BerquistSherman class paid adjustment causes NaN's[BUG] #537
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@jbogaardt I would be happy to help contribute on this or some of the other issues I brought up, let me know if that would be an option! |
@cdietrich215 , contributions are very welcome! Please contribute if you can! Any PR will be tested against the existing test suite. If you can keep those in a passing state, things should be good. Happy to provide further pointers on setting up a development environment (or alternatively you can reference https://chainladder-python.readthedocs.io/en/latest/library/contributing.html) |
Sounds good, was able to get the environment set up. Will make a PR on this soon. |
@jbogaardt Do I need additional permissions to do so? I'm getting permission denied errors when attempting to push a new branch: |
Additionally, this will replicate the error mentioned above. The code in the feature branch I'll push should prevent the issue.
|
Hi @cdietrich215, You wouldn't be able to push anything directly to casact/chainladder-python without being a project maintainer. However, this is easy to resolve. To submit a pull request (PR) to any public repository on GitHub, fork the repository, create a new branch on your fork, make your changes, commit them, push the changes to your fork, then navigate to the repository on GitHub and initiate a pull request by selecting the "Compare & pull request" option, choosing the appropriate branches, and providing a descriptive title and explanation for your changes; the repository maintainers will then review your PR and decide whether to merge your changes into the main codebase. |
Describe the bug
I believe the BerquistSherman class's paid adjustment has a bug (this probably some lesser used functionality again). This error occurs in the lookup array defined in line 127.
The procedure selects regression parameters (
a
andb
) within the same AY, based on how close the adjusted closed claim count is to the raw closed claim counts in the row, as described in the Friedland paper, page 289. However, in this implementation, if the adjusted closed count is higher than the max of the AY row's unadjusted closed counts, this lookup produces a value that extends into the "lower triangle". This causesnan
values for thea
andb
parameters, as well as the resulting paid loss values.There should be a ceiling on the lookup values corresponding to the final regression in the AY row. This can be done after lookup is defined in line 143:
Hope this makes sense - will try to produce an example at some point. Happy to help implement this.
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