Comments (4)
mages
commented on July 29, 2024
This might be possible, but I am not sure this would make sense from a statistical point of view. It sounds too much like a fudge.
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ludovictheate
commented on July 29, 2024
Hi Markus.
Apologies but I don't see why this would be a fudge. It is market best-practice (and also a main feature in many of the well known reserving software) to perform adjustments on the development factors (removing outliers, reducing the calibration history,...). Performing a bootstrap using the canonical factors when the "best-estimate" is computed using adjusted factors doesn't make a lot of sense.
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mages
commented on July 29, 2024
Hi Ludovic,
I am aware of the practice of selecting 'suitable' factors. However, it appears to me more based expert judgment, i.e. which data to include or exclude, rather than statistical, and therefore forcing a model to work with a given data set, instead of selecting a suitable model for the data at hand. Anyhow, that's more a philosophical point and what I would call 'fudging'.
Yet, I am happy to support you, if you would like to look into the implementation of your idea.
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trinostics
commented on July 29, 2024
The idea of using data to estimate the variability of a set of judgmentally
selected factors is basis of the paper I wrote with Bardis and Majidi that
extends the Mack/Murphy method in that situation. The paper is here
http://www.variancejournal.org/issues/06-02/143.pdf, Bardis' presentation
at a CAS meeting is here:
https://www.casact.org/education/spring/2013/handouts/Paper_2331_handout_970_0.pdf
I blogged about how to implement the technique with ChainLadder back in
2014 (
http://trinostics.blogspot.com/2013/07/implementing-clfm-with-chainladder.html
)
Ludovic appears to want to explore that possibility with the England's
Bootstrap method. We got pushback from some folks too. One should expect to
see a higher standard error of the prediction to the extent the selections
don't agree with the canonical factors, which is exactly what we found. If
that is the only dataset available for one's estimates, then it is nice to
have an algorithm that is able to respond with a scientifically based
result. Eventually we were able to convince our reviewers of the value of
our thesis.
…On Tue, Jun 19, 2018 at 9:15 AM, Markus Gesmann ***@***.***> wrote:
Hi Ludovic,
I am aware of the practice of selecting 'suitable' factors. However, it
appears to me more based expert judgment, i.e. which data to include or
exclude, rather than statistical, and therefore forcing a model to work
with a given data set, instead of selecting a suitable model for the data
at hand. Anyhow, that's more a philosophical point and what I would call
'fudging'.
Yet, I am happy to support you, if you would like to look into the
implementation of your idea.
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Related Issues (20)
- Mack chainladder provides different standarized residuals. HOT 8
- MultiChainLadder plot error when NA's in triangle HOT 1
- Triangle with empty values in early development periods HOT 27
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- dev.period order not correct when using plot(triang, lattice=TRUE) HOT 2
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- MackChainLadder: `tail = FALSE` works fine, `tail = TRUE` errors. HOT 2
- Cross Reference R/Python in README? HOT 4
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- Problems Loading on MacBook Pro OS 10.13 HOT 1
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