OpenBB/website/content/sdk/reference/econometrics/ols.md

title, description, keywords

title	description	keywords
ols	Perform OLS regression on timeseries data using the 'ols' function from OpenBB's econometrics module. The page includes parameters description, return type, and a practical example with code.	ols timeseries data statsmodels regression openbb.econometrics.ols Dependent variable series Independent variables series RegressionResultsWrapper OLS Regression Results R-squared Least Squares F-statistic Prob (F-statistic) Log-Likelihood AIC BIC Df Residuals Df Model Covariance Type

import HeadTitle from '@site/src/components/General/HeadTitle.tsx';

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.

---