statsmodels.sandbox.regression.gmm.GMMResults.conf_int
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GMMResults.conf_int(alpha=0.05, cols=None, method='default')
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Returns the confidence interval of the fitted parameters.
Parameters: alpha : float, optional
The significance level for the confidence interval. ie., The default
alpha
= .05 returns a 95% confidence interval.cols : array-like, optional
cols
specifies which confidence intervals to returnmethod : string
Not Implemented Yet Method to estimate the confidence_interval. ?Default? : uses self.bse which is based on inverse Hessian for MLE ?hjjh? : ?jac? : ?boot-bse? ?boot_quant? ?profile?
Returns: conf_int : array
Each row contains [lower, upper] limits of the confidence interval for the corresponding parameter. The first column contains all lower, the second column contains all upper limits.
Notes
The confidence interval is based on the standard normal distribution. Models wish to use a different distribution should overwrite this method.
Examples
>>> import statsmodels.api as sm >>> data = sm.datasets.longley.load() >>> data.exog = sm.add_constant(data.exog) >>> results = sm.OLS(data.endog, data.exog).fit() >>> results.conf_int() array([[-5496529.48322745, -1467987.78596704], [ -177.02903529, 207.15277984], [ -0.1115811 , 0.03994274], [ -3.12506664, -0.91539297], [ -1.5179487 , -0.54850503], [ -0.56251721, 0.460309 ], [ 798.7875153 , 2859.51541392]])
>>> results.conf_int(cols=(2,3)) array([[-0.1115811 , 0.03994274], [-3.12506664, -0.91539297]])
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