Statsmodels With Partly Identified Model

I am trying to run a regression where only some of the coefficients can be identified: data = np.array([[2, 1, 1, 1], [1, 1, 1, 0]]) df = pd.DataFrame(data, columns=['y', 'x1', 'x2

Solution 1:

statsmodels uses the Moore-Penrose generalized inverse pinv to estimate the parameters in linear regression model, OLS. WLS, GLS.

So, it provides a regularized solution if the design matrix is singular.

The covariance matrix of the parameter estimate has reduced rank, and only some linear combinations of parameters will be identified.

However, the model can be used for prediction, if the linear relationship in the data remains the same in prediction samples.