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statsmodels.regression.linear_model.OLSResults.conf_int_el

OLSResults.conf_int_el(param_num, sig=0.05, upper_bound=None, lower_bound=None, method='nm', stochastic_exog=1)[source]

Computes the confidence interval for the parameter given by param_num using Empirical Likelihood

Parameters:

param_num : float

The parameter for which the confidence interval is desired

sig : float

The significance level. Default is .05

upper_bound : float

The maximum value the upper limit can be. Default is the 99.9% confidence value under OLS assumptions.

lower_bound : float

The minimum value the lower limit can be. Default is the 99.9% confidence value under OLS assumptions.

method : string

Can either be ‘nm’ for Nelder-Mead or ‘powell’ for Powell. The optimization method that optimizes over nuisance parameters. Default is ‘nm’

Returns:

ci : tuple

The confidence interval

See also

el_test

Notes

This function uses brentq to find the value of beta where test_beta([beta], param_num)[1] is equal to the critical value.

The function returns the results of each iteration of brentq at each value of beta.

The current function value of the last printed optimization should be the critical value at the desired significance level. For alpha=.05, the value is 3.841459.

To ensure optimization terminated successfully, it is suggested to do el_test([lower_limit], [param_num])

If the optimization does not terminate successfully, consider switching optimization algorithms.

If optimization is still not successful, try changing the values of start_int_params. If the current function value repeatedly jumps from a number between 0 and the critical value and a very large number (>50), the starting parameters of the interior minimization need to be changed.

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