Summary not working for OLS estimation

I would like to thank user333800 for all the help!

For future reference if anyone comes across the same issue.

The following code:

df = pd.DataFrame({'RVFCAST':rv1fcast, 'RV1':rv1, 'RV5':rv5, 'RV22':rv22,})
df = df[df.RVFCAST != ""]
df = df.astype(float)

Model = smf.ols(formula='RVFCAST ~ RV1 + RV5 + RV22', data = df).fit()
mdl = Model.get_robustcov_results(cov_type='HAC',maxlags=1)

gave me:

print mdl.summary()
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                RVFCAST   R-squared:                       0.681
Model:                            OLS   Adj. R-squared:                  0.677
Method:                 Least Squares   F-statistic:                     120.9
Date:                Wed, 22 Apr 2015   Prob (F-statistic):           1.60e-48
Time:                        17:19:19   Log-Likelihood:                 1159.8
No. Observations:                 256   AIC:                            -2312.
Df Residuals:                     252   BIC:                            -2297.
Df Model:                           3                                         
Covariance Type:                  HAC                                         
==============================================================================
                 coef    std err          t      P>|t|      [95.0% Conf. Int.]
------------------------------------------------------------------------------
Intercept      0.0005      0.000      2.285      0.023      7.24e-05     0.001
RV1            0.2823      0.104      2.710      0.007         0.077     0.487
RV5           -0.0486      0.193     -0.252      0.802        -0.429     0.332
RV22           0.7450      0.232      3.212      0.001         0.288     1.202
==============================================================================
Omnibus:                      174.186   Durbin-Watson:                   2.045
Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2152.634
Skew:                           2.546   Prob(JB):                         0.00
Kurtosis:                      16.262   Cond. No.                     1.19e+03
==============================================================================

And I can now continue on my paper :)