chainladder.MackChainladder

class chainladder.MackChainladder

Basic stochastic chainladder method popularized by Thomas Mack

Parameters:

None

Attributes:

X_:

returns X

ultimate_:

The ultimate losses per the method

ibnr_:

The IBNR per the method

full_expectation_:

The ultimates back-filled to each development period in X replacing the known data

full_triangle_:

The ultimates back-filled to each development period in X retaining the known data

summary_:

summary of the model

full_std_err_:

The full standard error

total_process_risk_:

The total process error

total_parameter_risk_:

The total parameter error

mack_std_err_:

The total prediction error by origin period

total_mack_std_err_:

The total prediction error across all origin periods

Methods

fit(X[, y, sample_weight])	Fit the model with X.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
intersection(a, b)	Given two Triangles with mismatched indices, this method aligns their indices
predict(X[, sample_weight])	Predicts the Mack chainladder ultimate on a new triangle X.
set_backend(backend[, inplace, deep])	Converts triangle array_backend.
set_fit_request(*[, sample_weight])	Configure whether metadata should be requested to be passed to the fit method.
set_params(**params)	Set the parameters of this estimator.
set_predict_request(*[, sample_weight])	Configure whether metadata should be requested to be passed to the predict method.
to_json()	Serializes triangle object to json format
to_pickle(path[, protocol])	Serializes triangle object to pickle.

fit_predict
pipe
validate_X
validate_weight

Examples

Fit the Mack chainladder method and inspect the headline summary table, which combines the deterministic chainladder estimate with Mack’s stochastic standard error.

The deterministic chainladder ultimates match those of Chainladder. Mack’s contribution is the stochastic standard error in the rightmost column, which can be aggregated across origins.