M/Ph :dZddlZddlmZGddeZdS)a? Empirical Likelihood Linear Regression Inference The script contains the function that is optimized over nuisance parameters to conduct inference on linear regression parameters. It is called by eltest in OLSResults. General References ----------------- Owen, A.B.(2001). Empirical Likelihood. Chapman and Hall N) _OptFunctsc(eZdZdZdZ ddZdS) _ELRegOptsz A class that holds functions to be optimized over when conducting hypothesis tests and calculating confidence intervals. Parameters ---------- OLSResults : Results instance A fitted OLS result. cdS)N)selfs ]/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/statsmodels/emplike/elregress.py__init__z_ELRegOpts.__init__ s Nc |||<tjtjtj||} ||| <||d} | |_||tjtj|| z t|dz} | stj |ddd} tj ||zddd|z }|ddddf| z }||zddddf|z }tj ||fd}tj | |fd} tj t|d|z z}tj | jddd} ||| |}dtj|| jz}d|z dz|z |_tj tj||jz}d|zS#tjj$rtjcYSwxYw)a A function that is optimized over nuisance parameters to conduct a hypothesis test for the parameters of interest. Parameters ---------- nuisance_params: 1darray Parameters to be optimized over. Returns ------- llr : float -2 x the log-likelihood of the nuisance parameters and the hypothesized value of the parameter(s) of interest. r)axisNg?)npint_deletearangereshape new_paramssqueezedotintmeansum concatenateoneszerosshape _modif_newtonT new_weightsloglinalg LinAlgErrorinf)rnuisance_params param_numsendogexognobsnvarparamsb0_valsstochastic_exognuis_param_indexrest_vect exog_means exog_mom2 mean_est_vect mom2_est_vectregressor_est_vectwtsx0eta_stardenomllrs r _opt_nuis_regressz_ELRegOpts._opt_nuis_regress#sN&%z729RYt__-7$9$9::#2 ^^D!,, $ 2:bfT:6677 7 @ @TA N NO 3A...qrr2Jt !444abb9 !I ABBK*4M!D[!!!QRR%09rCsm  666666CCCCCCCCCCr