kpss

Perform the KPSS test for stationarity.

The KPSS null hypothesis is that the series IS stationary - the opposite of the ADF test - so running both tests gives a more robust read on stationarity.

Examples

from openbb import obb
obb.econometrics.kpss(column='close', data=[{'date': '2023-01-02', 'open': 110.0, 'high': 120.0, 'low': 100.0, 'close': 115.0, 'volume': 10000.0}, {'date': '2023-01-03', 'open': 165.0, 'high': 180.0, 'low': 150.0, 'close': 172.5, 'volume': 15000.0}, {'date': '2023-01-04', 'open': 146.67, 'high': 160.0, 'low': 133.33, 'close': 153.33, 'volume': 13333.33}, {'date': '2023-01-05', 'open': 137.5, 'high': 150.0, 'low': 125.0, 'close': 143.75, 'volume': 12500.0}, {'date': '2023-01-06', 'open': 132.0, 'high': 144.0, 'low': 120.0, 'close': 138.0, 'volume': 12000.0}])

Parameters

• standard

data: Data | DataFrame | Series | dict | list | list[DataFrame] | list[Data] | list[Series] | ndarray
Input dataset.

column: str
Column to test.

regression: str
Default: c
Null hypothesis: 'c' level-stationary, 'ct' trend-stationary.

nlags: int | str
Default: auto
Lags for the variance estimator: 'auto', 'legacy', or an explicit non-negative integer.

---

Returns

results: KpssData

Serializable results.

provider: str

Provider name.

warnings: Optional[list[Warning_]]

List of warnings.

chart: Optional[Chart]

Chart object.

extra: dict[str, Any]

Extra info.

---

Data

• standard

kpss_stat: float
Test statistic.

p_value: float
p-value of the test statistic.

p_value_interpolated: bool
False when p_value is a clamped table boundary (the true p-value is more extreme than reported).

lags: int
Number of lags used in the test.

critical_values: dict
Critical values at the 10%, 5%, 2.5%, and 1% levels.