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74 lines
2.9 KiB
Markdown
Vendored
74 lines
2.9 KiB
Markdown
Vendored
---
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title: ols
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description: OpenBB SDK Function
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---
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# ols
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Performs an OLS regression on timeseries data. [Source: Statsmodels]
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Source Code: [[link](https://github.com/OpenBB-finance/OpenBBTerminal/tree/main/openbb_terminal/econometrics/regression_model.py#L176)]
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```python
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openbb.econometrics.ols(Y: pd.DataFrame, X: pd.DataFrame)
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```
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---
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## Parameters
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| Name | Type | Description | Default | Optional |
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| ---- | ---- | ----------- | ------- | -------- |
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| Y | pd.DataFrame | Dependent variable series. | None | False |
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| X | pd.DataFrame | Dataframe of independent variables series. | None | False |
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---
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## Returns
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| Type | Description |
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| ---- | ----------- |
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| statsmodels.regression.linear_model.RegressionResultsWrapper | Regression model wrapper from statsmodels. |
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---
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## Examples
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```python
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import openbb_terminal.sdk as openbb
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df = openbb.econometrics.load("wage_panel")
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OLS_model = openbb.econometrics.OLS(df["lwage"], df[["educ", "exper", "expersq"]])
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print(OLS_model.summary())`
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```
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```
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OLS Regression Results
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=======================================================================================
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Dep. Variable: lwage R-squared (uncentered): 0.920
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Model: OLS Adj. R-squared (uncentered): 0.919
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Method: Least Squares F-statistic: 1.659e+04
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Date: Thu, 10 Nov 2022 Prob (F-statistic): 0.00
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Time: 15:28:11 Log-Likelihood: -3091.3
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No. Observations: 4360 AIC: 6189.
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Df Residuals: 4357 BIC: 6208.
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Df Model: 3
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Covariance Type: nonrobust
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==============================================================================
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coef std err t P>|t| [0.025 0.975]
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------------------------------------------------------------------------------
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educ 0.0986 0.002 39.879 0.000 0.094 0.103
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exper 0.1018 0.009 10.737 0.000 0.083 0.120
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expersq -0.0034 0.001 -4.894 0.000 -0.005 -0.002
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==============================================================================
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Omnibus: 1249.642 Durbin-Watson: 0.954
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Prob(Omnibus): 0.000 Jarque-Bera (JB): 9627.436
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Skew: -1.152 Prob(JB): 0.00
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Kurtosis: 9.905 Cond. No. 86.4
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==============================================================================
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Notes:
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[1] R² is computed without centering (uncentered) since the model does not contain a constant.
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[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.
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```
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---
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