ols_regression
Perform Ordinary Least Squares (OLS) regression.
OLS fits the best linear relationship between a dependent variable and one or more independent variables. Returns the estimated coefficient table - one row per regressor, with standard errors, t-statistics, p-values, and confidence intervals.
Examples
from openbb import obb
# Perform Ordinary Least Squares (OLS) regression.
stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()
obb.econometrics.ols_regression(data=stock_data, y_column="close", x_columns=["open", "high", "low"])
obb.econometrics.ols_regression(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.
cov_type: str
Default: nonrobust
Covariance estimator for the standard errors: 'nonrobust', or heteroskedasticity-robust 'HC0', 'HC1', 'HC2', or 'HC3'.
Returns
results: list[OlsRegressionData]
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
variable: str
Name of the regressor ('const' is the intercept).
coefficient: float
Estimated coefficient.
standard_error: float
Standard error of the coefficient.
t_statistic: float
t-statistic for the null hypothesis that the coefficient is zero.
p_value: float
Two-sided p-value of the t-statistic.
conf_int_lower: float
Lower bound of the 95% confidence interval.
conf_int_upper: float
Upper bound of the 95% confidence interval.