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Old 04-24-2019, 02:49 PM
MoralHazard MoralHazard is offline
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For CIs, it would probably also be reasonable to first back out Model 2's working 'midpoint' of each bin, by solving for x in [Model 2 Factor] = x^[Model 1 Factor], then just use the Model 1 CIs -- that is, get the upper bound of the CI as x^[Top of Model 1 CI].
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Old 04-24-2019, 03:06 PM
Actuarially Me Actuarially Me is offline
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Originally Posted by MoralHazard View Post
Sounds correct to me.

Yes, I think that sums it up.

Interesting question. This procedure really just produces restated model factors, but not standard errors. If you do need standard errors for the restated model, one thing you can try is bootstrapping this whole procedure. That is, take a random sampling of your training data with replacement, fit the first model, fit the second model on top of that, and save the resulting factors. Repeat 1000 times (or 10,000, or whatever is practical) and use the +/- 95% range of the distribution of the factors as your confidence interval. (Though I imagine there's probably some simpler way to get CIs.)
Perfect, thanks! I probably don't even need confidence intervals since the underwriters will probably ignore it anyways, but that sounds like a good path to go down.

Now have to find a way to streamline the process when underwriters make their adjustments.
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