MackChainladder Example

import pandas as pd
import chainladder as cl

This example demonstrates how you can can use the Mack Chainladder method.

# Load the data
data = cl.load_sample('raa')

# Compute Mack Chainladder ultimates and Std Err using 'volume' average
mack = cl.MackChainladder()
dev = cl.Development(average='volume')
mack.fit(dev.fit_transform(data))

plot_data = mack.summary_.to_frame(origin_as_datetime=False)
plot_data

	Latest	IBNR	Ultimate	Mack Std Err
1981	18834.0	NaN	18834.000000	NaN
1982	16704.0	153.953917	16857.953917	142.931716
1983	23466.0	617.370924	24083.370924	592.148304
1984	27067.0	1636.142163	28703.142163	712.853921
1985	26180.0	2746.736343	28926.736343	1452.090330
1986	15852.0	3649.103184	19501.103184	1994.987807
1987	12314.0	5435.302590	17749.302590	2203.838469
1988	13112.0	10907.192510	24019.192510	5354.340512
1989	5395.0	10649.984101	16044.984101	6331.543044
1990	2063.0	16339.442529	18402.442529	24565.775709

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import matplotlib.pyplot as plt
plt.style.use('ggplot')
%config InlineBackend.figure_format = 'retina'

ax = plot_data[['Latest', 'IBNR']].plot(
    kind='bar', stacked=True, ylim=(0, None), grid=True,
    yerr=pd.DataFrame({'latest': plot_data['Mack Std Err']*0,
                       'IBNR': plot_data['Mack Std Err']}),
    title='Mack Chainladder Ultimate',
    xlabel='Accident Year', ylabel='Loss');