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Yes, one easy way is to use the import pandas as pd
import numpy as np
import chainladder as cl
clrd = (
cl.load_sample("clrd")
.groupby("LOB")
.sum()
.loc["wkcomp", ["CumPaidLoss", "EarnedPremNet"]]
)
mack = cl.MackChainladder().fit(clrd["CumPaidLoss"])
mack.summary_
If we want to reference only a specific origin, for example 1990, we can do this: print(
"Mack's mean ultimate for origin 1990:",
mack.summary_.to_frame().loc["1990"]["Ultimate"],
)
print(
"Mack's standard error for origin 1990:",
mack.summary_.to_frame().loc["1990"]["Mack Std Err"],
) |
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In the tutorial we see how we can use the ibnr sum as the mean and the total mack std error as the sd to generate a normal distribution. If we wanted to get a normal dist of the Ultimate for each origin period using the data from the mack.summary_ would we use the ultimate and its corresponding mack std error for a specific origin period as the parameters for np.random.normal?
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