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 :)