Jarque-Bera Tests

jarque_bera_test_step(
    name: str,
    *,
    source: Literal[
        "states",
        "observables",
        "x_pred",
        "x_filt",
        "y_pred",
        "y_filt",
        "innov",
        "std_innov",
        "payload",
    ],
    filter_key: str = "filter",
    payload_key: str | None = None,
    column: Sequence[int] | int | None = None,
    burn_in: int = 0,
    drop_initial: bool = False,
    alpha: float = 0.05,
) -> MCStep

jarque_bera_test_step wraps run_jarque_bera_test(...). It selects a 1D array from generated data, a FilterResult, or a named payload, then runs the Jarque-Bera test for normality from the sample skewness and excess kurtosis.

Reference Distribution

The statistic is compared against a \(\chi^2(2)\) distribution asymptotically. For small samples the test uses a finite-sample critical-value lookup keyed on the sample size, so the reported degrees of freedom carry the sample size rather than a fixed value.

Sources:

Source	Description
states	Use context.data.states. Set drop_initial=True to remove the initial state row.
observables	Use context.data.observables.
x_pred, x_filt, y_pred, y_filt, innov, std_innov	Read arrays from the FilterResult stored under filter_key.
payload	Read an array-like object from context.payloads[payload_key].