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