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Version: v5

causality

Perform the Granger causality test to assess whether X helps predict y.

Granger causality tests whether past values of one series improve the forecast of another. It is a predictive, not a structural, notion of causality.

Examples​

from openbb import obb
# Granger causality test with mock data.
obb.econometrics.causality(y_column='close', x_column='open', lag=1, 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​

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

y_column: str
Target column.

x_column: str
Candidate causal column.

lag: int
Default: 3
Number of lags to test.

Returns​

results: list[CausalityData]

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​

test: str
Name of the Granger sub-test.

statistic: float
Test statistic.

p_value: float
p-value of the test statistic.

lag: int
Number of lags tested.

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