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Version: v5

variance_inflation_factor

Calculate the variance inflation factor (VIF) to detect multicollinearity.

VIF quantifies how much the variance of an estimated coefficient is inflated by collinearity with the other regressors. A VIF between 1 and 5 is generally acceptable; values above 5 indicate high collinearity.

Examples​

from openbb import obb
# Calculate the variance inflation factor.
stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()
obb.econometrics.variance_inflation_factor(data=stock_data, columns=["open", "high", "low", "close"])
obb.econometrics.variance_inflation_factor(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: Data | DataFrame | Series | dict | list | list[DataFrame] | list[Data] | list[Series] | ndarray
Input dataset.

columns: list[str]
Columns to test for collinearity. If omitted, all numeric columns.

Returns​

results: list[VarianceInflationFactorData]

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​

variable: str
Name of the variable.

vif: float
Variance inflation factor (above 5 indicates high collinearity).

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