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residual_autocorrelation

Perform Breusch-Godfrey Lagrange Multiplier tests for residual autocorrelation.

The Breusch-Godfrey Lagrange Multiplier test is a sophisticated tool for uncovering autocorrelation within the residuals of a regression model. Autocorrelation in residuals can indicate that a model fails to capture some aspect of the underlying data structure, possibly leading to biased or inefficient estimates. By specifying the number of lags, you can control the depth of the test to check for autocorrelation, allowing for a tailored analysis that matches the specific characteristics of your data. This test is particularly valuable in econometrics and time-series analysis, where understanding the independence of errors is crucial for model validity.

Examples​

from openbb import obb
# Perform Breusch-Godfrey Lagrange Multiplier tests for residual autocorrelation.
stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()
obb.econometrics.residual_autocorrelation(data=stock_data, y_column="close", x_columns=["open", "high", "low"])
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​

data: list[openbb_core.provider.abstract.data.Data]
Input dataset.

y_column: str
Target column.

x_columns: list[str]
list of columns to use as exogenous variables.

lags: int
Default: 1
Number of lags to use in the test.

Returns​

results: list[Data]

Serializable results.

provider: None

Provider name.

warnings: Optional[list[Warning_]]

list of warnings.

chart: Optional[Chart]

Chart object.

extra: dict[str, Any]

Extra info.

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