Quick Start

Important Links

• CCP Overview

• Pre-Estimation Checks

• Simulation Study

• Counterfactuals

• Bus Engine Example

This page shows the public CCP wrapper. The snippet checks that the estimator runs and returns the structural objects. The pre-estimation page covers support and first-stage CCP quality.

The public API follows the same sklearn convention as NFXP. Create an estimator, call fit, and read fitted attributes.

from econirl.datasets import load_rust_bus, rust_bus_reward_spec
from econirl import CCP

df = load_rust_bus()

model = CCP(
    n_states=90,
    discount=0.9999,
    utility=rust_bus_reward_spec(90),
    num_policy_iterations=3,
)
model.fit(df, state="mileage_bin", action="replaced", id="bus_id")

for name in model.params_:
    print(f"{name}: estimate={model.params_[name]:.6f}, se={model.se_[name]:.6f}")
print(f"policy_shape={model.policy_.shape}")
print(f"transition_source={model.transition_source_}")
print(f"termination={model.termination_reason_}")
print(f"parameter_residual={model.npl_parameter_residual_:.6e}")
print(f"policy_residual={model.npl_policy_residual_:.6e}")

Result

operating_cost: estimate=0.000995, se=0.000421
replacement_cost: estimate=3.072211, se=0.074237
policy_shape=(90, 2)
transition_source=estimated from fitted panel
termination=fixed_k_complete
parameter_residual=1.452655e-02
policy_residual=2.148578e-02

The fitted estimator exposes structural parameters, standard errors, a policy, a value function, and a likelihood.

Attribute	Meaning
params_	Estimated structural reward parameters.
se_	Standard errors for the structural parameters.
coef_	Coefficients as a numpy array.
policy_	Estimated action probabilities by state.
value_	Estimated policy value function by state.
transition_tensor_	Transition probabilities used for each action.
transition_source_	Whether transitions came from the panel or were supplied.
log_likelihood_	CCP pseudo-log-likelihood at the fitted parameters.
converged_	Whether the requested CCP run completed successfully.
npl_converged_	Whether both NPL residuals met the stopping tolerance.
termination_reason_	Why the CCP run stopped.
npl_parameter_residual_	Final L2 change in reward parameters.
npl_policy_residual_	Final maximum absolute policy change.

Set num_policy_iterations=1 for a one-step Hotz-Miller estimate. Set it to a larger positive integer for a maximum number of NPL updates. The run can stop early only when both residuals meet the tolerance. Set it to -1 to require that joint fixed point before the iteration cap.

Model-based, robust, and clustered standard errors are conditional on the policy object used in the final pseudo-likelihood and on the transition tensor. The pairs-cluster bootstrap repeats empirical CCP estimation after resampling individuals, but it still holds the transition tensor fixed.

Counterfactual Example

cf = model.counterfactual(replacement_cost=4.0)

print(f"replacement_cost={cf.params['replacement_cost']:.6f}")
print(f"P(replace | state=50)={cf.policy[50, 1]:.6f}")

Result

replacement_cost=4.000000
P(replace | state=50)=0.054908

This solves the fitted model again with a higher replacement cost and returns the new value function and policy.

Advanced API

Use econirl.estimation.ccp.CCPEstimator when you need direct control over panel objects, utility objects, transition tensors, CCP smoothing, NPL stopping rules, or diagnostic metadata.