Statsmodels: How can I select different confidence intervals for regressions - regression

Question

I want to run a regression with 99% confidence interval instead of the default 95% using statsmodels.
I looked at the documentation if there is an argument in the fit() method but I didn't notice something. I tried also the conf_int method but I am confused from the output.
import pandas as pd
import math
import statsmodels.formula.api as sm
df = pd.read_excel(r'C:\TestData.xlsx')
df['LogBalance'] = df['Balance'].map(lambda x: math.log(x))
est = sm.ols(formula= 'LogBalance ~ N + Rate',
data=df).fit(cov_type='HAC',cov_kwds={'maxlags':1})
print(est.summary())
print(est.conf_int(alpha=0.01, cols=None))
Since I am new in Python can you tell me if and how can I perform a regression in statsmodels with adjusted confidence intervals if possible in the initial regression output?
Thanks

Answer 1

You can specify the confidence interval in .summary() directly Please consider the following example:
import statsmodels.formula.api as smf
import seaborn as sns
# load a sample dataset
df = sns.load_dataset('tips')
# run model
formula = 'tip ~ size + total_bill'
results = smf.ols(formula=formula, data=df).fit()
# use 95 % CI (default setting)
print(results.summary())
OLS Regression Results
==============================================================================
Dep. Variable: tip R-squared: 0.468
Model: OLS Adj. R-squared: 0.463
Method: Least Squares F-statistic: 105.9
Date: Fri, 21 Jun 2019 Prob (F-statistic): 9.67e-34
Time: 21:42:09 Log-Likelihood: -347.99
No. Observations: 244 AIC: 702.0
Df Residuals: 241 BIC: 712.5
Df Model: 2
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
Intercept 0.6689 0.194 3.455 0.001 0.288 1.050
size 0.1926 0.085 2.258 0.025 0.025 0.361
total_bill 0.0927 0.009 10.172 0.000 0.075 0.111
==============================================================================
Omnibus: 24.753 Durbin-Watson: 2.100
Prob(Omnibus): 0.000 Jarque-Bera (JB): 46.169
Skew: 0.545 Prob(JB): 9.43e-11
Kurtosis: 4.831 Cond. No. 67.6
==============================================================================
# use 99 % CI
print(results.summary(alpha=0.01))
OLS Regression Results
==============================================================================
Dep. Variable: tip R-squared: 0.468
Model: OLS Adj. R-squared: 0.463
Method: Least Squares F-statistic: 105.9
Date: Fri, 21 Jun 2019 Prob (F-statistic): 9.67e-34
Time: 21:45:57 Log-Likelihood: -347.99
No. Observations: 244 AIC: 702.0
Df Residuals: 241 BIC: 712.5
Df Model: 2
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.005 0.995]
------------------------------------------------------------------------------
Intercept 0.6689 0.194 3.455 0.001 0.166 1.172
size 0.1926 0.085 2.258 0.025 -0.029 0.414
total_bill 0.0927 0.009 10.172 0.000 0.069 0.116
==============================================================================
Omnibus: 24.753 Durbin-Watson: 2.100
Prob(Omnibus): 0.000 Jarque-Bera (JB): 46.169
Skew: 0.545 Prob(JB): 9.43e-11
Kurtosis: 4.831 Cond. No. 67.6
==============================================================================