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Version: v5

panel_between

Estimate a Between regression on panel data.

The Between estimator regresses the entity-level means, summarising the long-run cross-sectional relationship between the regressors and the target.

Examples​

from openbb import obb
obb.econometrics.panel_between(y_column='portfolio_value', x_columns=['risk_free_rate'], data=[{'is_multiindex': True, 'multiindex_names': "['asset_manager', 'time']", 'asset_manager': 'asset_manager_1', 'time': 0, 'portfolio_value': 100000.0, 'risk_free_rate': 0.02}, {'is_multiindex': True, 'multiindex_names': "['asset_manager', 'time']", 'asset_manager': 'asset_manager_1', 'time': 1, 'portfolio_value': 150000.0, 'risk_free_rate': 0.03}, {'is_multiindex': True, 'multiindex_names': "['asset_manager', 'time']", 'asset_manager': 'asset_manager_2', 'time': 0, 'portfolio_value': 150000.0, 'risk_free_rate': 0.03}, {'is_multiindex': True, 'multiindex_names': "['asset_manager', 'time']", 'asset_manager': 'asset_manager_2', 'time': 1, 'portfolio_value': 133333.33, 'risk_free_rate': 0.03}, {'is_multiindex': True, 'multiindex_names': "['asset_manager', 'time']", 'asset_manager': 'asset_manager_3', 'time': 0, 'portfolio_value': 133333.33, 'risk_free_rate': 0.03}, {'is_multiindex': True, 'multiindex_names': "['asset_manager', 'time']", 'asset_manager': 'asset_manager_3', 'time': 1, 'portfolio_value': 125000.0, 'risk_free_rate': 0.03}, {'is_multiindex': True, 'multiindex_names': "['asset_manager', 'time']", 'asset_manager': 'asset_manager_4', 'time': 0, 'portfolio_value': 125000.0, 'risk_free_rate': 0.03}, {'is_multiindex': True, 'multiindex_names': "['asset_manager', 'time']", 'asset_manager': 'asset_manager_4', 'time': 1, 'portfolio_value': 120000.0, 'risk_free_rate': 0.02}, {'is_multiindex': True, 'multiindex_names': "['asset_manager', 'time']", 'asset_manager': 'asset_manager_5', 'time': 0, 'portfolio_value': 120000.0, 'risk_free_rate': 0.02}, {'is_multiindex': True, 'multiindex_names': "['asset_manager', 'time']", 'asset_manager': 'asset_manager_5', 'time': 1, 'portfolio_value': 116666.67, 'risk_free_rate': 0.02}])

Parameters​

data: Data | DataFrame | Series | dict | list | list[DataFrame] | list[Data] | list[Series] | ndarray
Input panel dataset, indexed by entity and time.

y_column: str
Name of the dependent (target) column.

x_columns: list[str]
Names of the independent (exogenous) columns.

cov_type: str
Default: unadjusted
Covariance estimator: 'unadjusted' (homoskedastic) or 'robust' (heteroskedasticity-robust standard errors).

Returns​

results: list[PanelRegressionData]

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 regressor.

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
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.

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