@@ -90,7 +90,6 @@ def fit_qte(y, x, d, quantiles, learner_g, learner_m, all_smpls, n_rep=1,
9090
9191def boot_qte (scaled_scores , ses , quantiles , all_smpls , n_rep , bootstrap , n_rep_boot ):
9292 n_quantiles = len (quantiles )
93- boot_qte = np .zeros ((n_quantiles , n_rep_boot * n_rep ))
9493 boot_t_stat = np .zeros ((n_quantiles , n_rep_boot * n_rep ))
9594 for i_rep in range (n_rep ):
9695 n_obs = scaled_scores .shape [0 ]
@@ -99,11 +98,10 @@ def boot_qte(scaled_scores, ses, quantiles, all_smpls, n_rep, bootstrap, n_rep_b
9998 i_start = i_rep * n_rep_boot
10099 i_end = (i_rep + 1 ) * n_rep_boot
101100
102- boot_qte [i_quant , i_start :i_end ] = np .matmul (weights , scaled_scores [:, i_quant , i_rep ]) / n_obs
103101 boot_t_stat [i_quant , i_start :i_end ] = np .matmul (weights , scaled_scores [:, i_quant , i_rep ]) / \
104102 (n_obs * ses [i_quant , i_rep ])
105103
106- return boot_qte , boot_t_stat
104+ return boot_t_stat
107105
108106
109107def confint_qte (coef , se , quantiles , boot_t_stat = None , joint = True , level = 0.95 ):
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