GLM Reserving

import chainladder as cl
import pandas as pd

This example demonstrates how you can use the TweedieGLM estimator to incorporate the GLM framework into a chainladder workflow. The specific case of the Over-dispersed poisson GLM fit to incremental paids. It is further shown to match the basic chainladder Development estimator.

genins = cl.load_sample("genins")

# Fit an ODP GLM
dev = cl.TweedieGLM(
    design_matrix="C(development) + C(origin)", link="log", power=1
).fit(genins)

# Grab LDFs vs traditional approach
glm = dev.ldf_.iloc[..., 0, :].T.iloc[:, 0].rename("GLM")
traditional = cl.Development().fit(genins).ldf_.T.iloc[:, 0].rename("Traditional")

# Plot data
results = pd.concat((glm, traditional), axis=1)

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import matplotlib.pyplot as plt

plt.style.use("ggplot")
%config InlineBackend.figure_format = 'retina'

ax = results.plot(kind="bar", title="LDF: Poisson GLM vs Traditional");