U Kv f ã@s,dZddlZddlmZGdd„deƒZdS)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 éN)Ú _OptFunctsc@s"eZdZdZdd„Zddd„ZdS)Ú _ELRegOptszæ 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. cCsdS)N©)ÚselfrrúA/tmp/pip-unpacked-wheel-2v6byqio/statsmodels/emplike/elregress.pyÚ__init__ sz_ELRegOpts.__init__Nc Cs˜|||<t t t |¡|¡¡} ||| <| |d¡} | |_||t t || ¡¡ t|ƒd¡} | sðtj |dddd…} tj ||dddd…|}|dd…dd…f| }||dd…dd…f|}tj ||fdd}tj | |fdd} t  t|ƒ¡d|}t  | jd¡ dd¡}zR| || |¡}dt || j¡}d|d||_t  t ||j¡¡}d|WStjjjk r’tjYSXdS)a½ 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. ér)ZaxisNgð?éÿÿÿÿéþÿÿÿ)ÚnpÚint_ÚdeleteZarangeZreshapeÚ new_paramsZsqueezeÚdotÚintZmeanÚsumZ concatenateZonesÚzerosÚshapeZ _modif_newtonÚTZ new_weightsÚlogZlinalgZ LinAlgErrorÚinf)rZnuisance_paramsZ param_numsZendogZexogZnobsZnvarÚparamsZb0_valsZstochastic_exogZnuis_param_indexrZest_vectZ exog_meansZ exog_mom2Z mean_est_vectZ mom2_est_vectZregressor_est_vectZwtsZx0Zeta_starZdenomZllrrrrÚ_opt_nuis_regress#sBÿ  ÿÿ ÿ ÿ z_ELRegOpts._opt_nuis_regress)NNNNNNNN)Ú__name__Ú __module__Ú __qualname__Ú__doc__rrrrrrrs ýr)rZnumpyr Zstatsmodels.emplike.descriptiverrrrrrÚs