residual_autocorrelation
Perform the Breusch-Godfrey LM test for residual autocorrelation.
The Breusch-Godfrey test detects autocorrelation in regression residuals up to a chosen number of lags. Significant statistics indicate the model has not captured some of the data's serial dependence.
Examples
from openbb import obb
obb.econometrics.residual_autocorrelation(y_column='close', x_columns=['open', 'high', 'low'], 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.
y_column: str
Name of the dependent (target) column.
x_columns: list[str]
Names of the independent (exogenous) columns.
lags: int
Default: 1
Number of lags to include in the test.
Returns
results: ResidualAutocorrelationData
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
lm_statistic: float
Lagrange Multiplier test statistic.
lm_p_value: float
p-value of the LM statistic.
f_statistic: float
F-statistic of the auxiliary regression.
f_p_value: float
p-value of the F-statistic.