gretl version 1.10.0cvs Current session: 2014-10-22 20:13 # ------------------ estimation modèle 7 ? list x = TAILLE INV_ANT CF_ANT VOLAT D_DIV SCOREZ D_V DRD MTB RFR TANGI \ AGE_BASE REND TAXR DFC_IND2 PROFI SIC2DIGIT Generated list x ? list x1 = const POT1 x Generated list x1 ? list x2 = const POT2 x Generated list x2 ? smpl (ok(x1) && ok(x2)) --restrict Full data set: 2590 observations Current sample: 296 observations ? biprobit external equity x1 ; x2 --save-xbeta --robust --cluster=i_firm Successive criterion values within tolerance (1e-006) Model 1: Bivariate probit, using observations 1-296 Standard errors clustered by 82 values of i_firm coefficient std. error z p-value ---------------------------------------------------------- external: const 0.110160 1.48425 0.07422 0.9408 POT1 7.13424 1.18635 6.014 1.81e-09 *** TAILLE 0.0361287 0.0680595 0.5308 0.5955 INV_ANT −0.265339 0.516228 −0.5140 0.6073 CF_ANT 2.09666 1.30696 1.604 0.1087 VOLAT 0.781673 5.24522 0.1490 0.8815 D_DIV −0.297728 0.254035 −1.172 0.2412 SCOREZ −0.271968 0.331663 −0.8200 0.4122 D_V −0.236385 1.11381 −0.2122 0.8319 DRD −2.41504 3.57942 −0.6747 0.4999 MTB 0.566456 0.112937 5.016 5.28e-07 *** RFR −0.0256260 0.196319 −0.1305 0.8961 TANGI −0.0753952 1.00524 −0.07500 0.9402 AGE_BASE −0.0356837 0.0216510 −1.648 0.0993 * REND −0.246472 0.222779 −1.106 0.2686 TAXR −0.120172 0.521025 −0.2306 0.8176 DFC_IND2 −1.57189 3.78622 −0.4152 0.6780 PROFI −2.72475 2.78955 −0.9768 0.3287 SIC2DIGIT 0.0622110 0.0667275 0.9323 0.3512 equity: const 6.97190 2.69931 2.583 0.0098 *** POT2 4.30132 0.962711 4.468 7.90e-06 *** TAILLE −0.467652 0.179287 −2.608 0.0091 *** INV_ANT 2.95192 1.09282 2.701 0.0069 *** CF_ANT −1.17203 2.01875 −0.5806 0.5615 VOLAT 22.1561 7.75291 2.858 0.0043 *** D_DIV −1.24048 0.431633 −2.874 0.0041 *** SCOREZ −1.53191 0.651642 −2.351 0.0187 ** D_V 0.960271 1.29438 0.7419 0.4582 DRD −4.39569 7.20260 −0.6103 0.5417 MTB 0.378008 0.147044 2.571 0.0101 ** RFR −0.420355 0.323356 −1.300 0.1936 TANGI −7.06169 2.42802 −2.908 0.0036 *** AGE_BASE 0.0755413 0.0532725 1.418 0.1562 REND 0.0585621 0.257865 0.2271 0.8203 TAXR −0.914275 1.10068 −0.8306 0.4062 DFC_IND2 −3.22315 6.17726 −0.5218 0.6018 PROFI −21.6354 6.27013 −3.451 0.0006 *** SIC2DIGIT −0.125967 0.0890904 −1.414 0.1574 Log-likelihood −128.5415 Akaike criterion 335.0830 Schwarz criterion 479.0070 Hannan-Quinn 392.7073 rho = 1 Test of independence - Null hypothesis: rho = 0 Test statistic: Chi-square(1) = 16.4347 with p-value = 5.03547e-005 # genr matrix predict_prob = $yhat ? genr series predict_external = $yhat[,1] Replaced series predict_external (ID 63) ? genr series predict_equity = $yhat[,2] Replaced series predict_equity (ID 64) ? genr series prob_external = cdf(N,predict_external) Replaced series prob_external (ID 65) ? genr series prob_equity = cdf(N, predict_equity) Replaced series prob_equity (ID 66) ? genr correct_ext1 = sum((prob_external>=0.5) && external=1)/sum(external=1) Generated scalar correct_ext1 = 0.512195 ? genr correct_ext0 = sum((prob_external<0.5) && external=0)/sum(external=0) Generated scalar correct_ext0 = 0.96729 ? genr correct_ext = sum((prob_external>=0.5)==external)/$nobs Generated scalar correct_ext = 0.841216 ? smpl external=1 --restrict Full data set: 2590 observations Current sample: 82 observations ? genr correct_eqt1 = sum((prob_equity>=0.5) && equity=1)/sum(equity=1) Generated scalar correct_eqt1 = 0.454545 ? genr correct_eqt0 = sum((prob_equity<0.5) && equity=0)/sum(equity=0) Generated scalar correct_eqt0 = 0.985915 ? genr correct_eqt = sum((prob_equity>=0.5)==equity)/$nobs Generated scalar correct_eqt = 0.914634 ? smpl full Full data range: 1 - 2590 (n = 2590) # Impression des résultats de l'estimation ? print correct_ext1 correct_ext0 correct_ext correct_eqt1 correct_eqt0 \ correct_eqt correct_ext1 = 0.51219512 correct_ext0 = 0.96728972 correct_ext = 0.84121622 correct_eqt1 = 0.45454545 correct_eqt0 = 0.98591549 correct_eqt = 0.91463415 ----------------------------------- RUN 2 - NO CHANGES OCCURRED ? list x = TAILLE INV_ANT CF_ANT VOLAT D_DIV SCOREZ D_V DRD MTB RFR TANGI \ AGE_BASE REND TAXR DFC_IND2 PROFI SIC2DIGIT Replaced list x ? list x1 = const POT1 x Replaced list x1 ? list x2 = const POT2 x Replaced list x2 ? smpl (ok(x1) && ok(x2)) --restrict Full data set: 2590 observations Current sample: 296 observations ? biprobit external equity x1 ; x2 --save-xbeta --robust --cluster=i_firm Successive criterion values within tolerance (1e-006) Model 2: Bivariate probit, using observations 1-296 Standard errors clustered by 82 values of i_firm coefficient std. error z p-value ---------------------------------------------------------- external: const 0.110160 1.48425 0.07422 0.9408 POT1 7.13424 1.18635 6.014 1.81e-09 *** TAILLE 0.0361287 0.0680595 0.5308 0.5955 INV_ANT −0.265339 0.516228 −0.5140 0.6073 CF_ANT 2.09666 1.30696 1.604 0.1087 VOLAT 0.781673 5.24522 0.1490 0.8815 D_DIV −0.297728 0.254035 −1.172 0.2412 SCOREZ −0.271968 0.331663 −0.8200 0.4122 D_V −0.236385 1.11381 −0.2122 0.8319 DRD −2.41504 3.57942 −0.6747 0.4999 MTB 0.566456 0.112937 5.016 5.28e-07 *** RFR −0.0256260 0.196319 −0.1305 0.8961 TANGI −0.0753952 1.00524 −0.07500 0.9402 AGE_BASE −0.0356837 0.0216510 −1.648 0.0993 * REND −0.246472 0.222779 −1.106 0.2686 TAXR −0.120172 0.521025 −0.2306 0.8176 DFC_IND2 −1.57189 3.78622 −0.4152 0.6780 PROFI −2.72475 2.78955 −0.9768 0.3287 SIC2DIGIT 0.0622110 0.0667275 0.9323 0.3512 equity: const 6.97190 2.69931 2.583 0.0098 *** POT2 4.30132 0.962711 4.468 7.90e-06 *** TAILLE −0.467652 0.179287 −2.608 0.0091 *** INV_ANT 2.95192 1.09282 2.701 0.0069 *** CF_ANT −1.17203 2.01875 −0.5806 0.5615 VOLAT 22.1561 7.75291 2.858 0.0043 *** D_DIV −1.24048 0.431633 −2.874 0.0041 *** SCOREZ −1.53191 0.651642 −2.351 0.0187 ** D_V 0.960271 1.29438 0.7419 0.4582 DRD −4.39569 7.20260 −0.6103 0.5417 MTB 0.378008 0.147044 2.571 0.0101 ** RFR −0.420355 0.323356 −1.300 0.1936 TANGI −7.06169 2.42802 −2.908 0.0036 *** AGE_BASE 0.0755413 0.0532725 1.418 0.1562 REND 0.0585621 0.257865 0.2271 0.8203 TAXR −0.914275 1.10068 −0.8306 0.4062 DFC_IND2 −3.22315 6.17726 −0.5218 0.6018 PROFI −21.6354 6.27013 −3.451 0.0006 *** SIC2DIGIT −0.125967 0.0890904 −1.414 0.1574 Log-likelihood −128.5415 Akaike criterion 335.0830 Schwarz criterion 479.0070 Hannan-Quinn 392.7073 rho = 1 Test of independence - Null hypothesis: rho = 0 Test statistic: Chi-square(1) = 16.4347 with p-value = 5.03547e-005 # genr matrix predict_prob = $yhat ? genr series predict_external = $yhat[,1] Replaced series predict_external (ID 63) ? genr series predict_equity = $yhat[,2] Replaced series predict_equity (ID 64) ? genr series prob_external = cdf(N,predict_external) Replaced series prob_external (ID 65) ? genr series prob_equity = cdf(N, predict_equity) Replaced series prob_equity (ID 66) ? genr correct_ext1 = sum((prob_external>=0.5) && external=1)/sum(external=1) Replaced scalar correct_ext1 = 0.512195 ? genr correct_ext0 = sum((prob_external<0.5) && external=0)/sum(external=0) Replaced scalar correct_ext0 = 0.96729 ? genr correct_ext = sum((prob_external>=0.5)==external)/$nobs Replaced scalar correct_ext = 0.841216 ? smpl external=1 --restrict Full data set: 2590 observations Current sample: 82 observations ? genr correct_eqt1 = sum((prob_equity>=0.5) && equity=1)/sum(equity=1) Replaced scalar correct_eqt1 = 0.454545 ? genr correct_eqt0 = sum((prob_equity<0.5) && equity=0)/sum(equity=0) Replaced scalar correct_eqt0 = 0.985915 ? genr correct_eqt = sum((prob_equity>=0.5)==equity)/$nobs Replaced scalar correct_eqt = 0.914634 ? smpl full Full data range: 1 - 2590 (n = 2590) # Impression des résultats de l'estimation ? print correct_ext1 correct_ext0 correct_ext correct_eqt1 correct_eqt0 \ correct_eqt correct_ext1 = 0.51219512 correct_ext0 = 0.96728972 correct_ext = 0.84121622 correct_eqt1 = 0.45454545 correct_eqt0 = 0.98591549 correct_eqt = 0.91463415 ----------------------------------- RUN 3 - Verbose ? list x = TAILLE INV_ANT CF_ANT VOLAT D_DIV SCOREZ D_V DRD MTB RFR TANGI \ AGE_BASE REND TAXR DFC_IND2 PROFI SIC2DIGIT Replaced list x ? list x1 = const POT1 x Replaced list x1 ? list x2 = const POT2 x Replaced list x2 ? smpl (ok(x1) && ok(x2)) --restrict Full data set: 2590 observations Current sample: 296 observations ? biprobit external equity x1 ; x2 --verbose --save-xbeta --robust \ --cluster=i_firm Iteration 1: loglikelihood = -126.732319570 (steplength = 1) Parameters: 0.30456 3.7318 -0.0056409 -0.11407 0.73130 1.4001 -0.19232 -0.22081 -0.41888 -0.60670 0.33540 0.00056145 -0.15274 -0.013703 -0.11472 -0.21560 -0.68040 -0.60375 0.029995 Gradients: -225.38 35.355 -2658.3 -46.967 -48.452 -4.7018 -183.99 -336.77 -53.507 -5.9215 -377.49 -353.63 -55.879 -4492.0 -26.460 -82.593 -49.146 -18.681 -1061.6 (norm 6.04e+000) Iteration 2: loglikelihood = -116.854957675 (steplength = 1) Parameters: 0.34163 6.0807 0.010068 -0.23558 1.5343 2.9072 -0.24238 -0.30186 -0.40942 -1.7480 0.50877 -0.015752 -0.19070 -0.026719 -0.22146 -0.26727 -1.2620 -1.9328 0.056247 Gradients: -23.968 6.8701 -278.75 -3.8765 -4.7662 -0.48700 -19.490 -39.674 -5.9908 -0.85857 -31.911 -43.458 -5.1350 -471.05 -1.9053 -8.2734 -5.2663 -1.9668 -103.01 (norm 2.64e+000) Iteration 3: loglikelihood = -115.959422738 (steplength = 1) Parameters: 0.30892 7.1302 0.019579 -0.28869 1.8496 3.4584 -0.25586 -0.31740 -0.32406 -2.4688 0.58327 -0.019622 -0.077858 -0.032896 -0.27326 -0.22249 -1.5644 -2.8691 0.068263 Gradients: -3.5930 1.5769 -39.998 -0.51541 -0.77993 -0.091275 -2.8437 -6.8115 -1.0150 -0.19892 -5.5161 -7.6243 -0.51352 -66.552 -0.39267 -1.0756 -0.78307 -0.37986 -14.884 (norm 1.21e+000) Iteration 4: loglikelihood = -115.950074936 (steplength = 1) Parameters: 0.30123 7.2532 0.020483 -0.29460 1.8852 3.4882 -0.25722 -0.31876 -0.31499 -2.5441 0.59213 -0.019151 -0.061960 -0.033572 -0.27929 -0.21387 -1.5878 -2.9856 0.069803 Gradients: -0.27425 0.14446 -3.0599 -0.032266 -0.059699 -0.0081430 -0.21854 -0.54795 -0.084016 -0.017125 -0.42295 -0.60574 -0.026217 -4.9924 -0.037708 -0.067156 -0.059201 -0.032005 -1.0639 (norm 3.53e-001) Iteration 5: loglikelihood = -115.950073895 (steplength = 1) Parameters: 0.30109 7.2546 0.020492 -0.29466 1.8856 3.4883 -0.25723 -0.31877 -0.31491 -2.5448 0.59222 -0.019134 -0.061801 -0.033579 -0.27936 -0.21376 -1.5879 -2.9868 0.069822 Gradients: -0.0027497 0.0014823 -0.031387 -0.00029759 -0.00057514-8.4750e-005 -0.0022154 -0.0054528 -0.00083026 -0.00015405 -0.0038081 -0.0057266 -0.00027270 -0.051852 -0.00042101 -0.00060152 -0.00058864 -0.00030506 -0.010043 (norm 3.52e-002) Iteration 6: loglikelihood = -115.950073895 (steplength = 0.5) Parameters: 0.30109 7.2546 0.020492 -0.29466 1.8856 3.4883 -0.25723 -0.31877 -0.31491 -2.5448 0.59222 -0.019134 -0.061801 -0.033579 -0.27936 -0.21376 -1.5879 -2.9868 0.069822 Gradients: -3.0884e-007 1.6613e-007-3.5403e-006-3.2291e-008-6.3017e-008-1.0351e-008 -2.4655e-007-6.1279e-007-8.9608e-008-1.4960e-008-4.0093e-007-6.0793e-007 -3.3477e-008-5.8968e-006-4.8847e-008-6.2128e-008-6.5420e-008-3.2586e-008 -1.0717e-006 (norm 3.71e-004) --- FINAL VALUES: loglikelihood = -115.950073895 (steplength = 0.5) Parameters: 0.30109 7.2546 0.020492 -0.29466 1.8856 3.4883 -0.25723 -0.31877 -0.31491 -2.5448 0.59222 -0.019134 -0.061801 -0.033579 -0.27936 -0.21376 -1.5879 -2.9868 0.069822 Gradients: -1.5442e-007 8.3064e-008-1.7701e-006-1.6146e-008-3.1509e-008-5.1755e-009 -1.2328e-007-3.0639e-007-4.4804e-008-7.4802e-009-2.0046e-007-3.0396e-007 -1.6738e-008-2.9484e-006-2.4423e-008-3.1064e-008-3.2710e-008-1.6293e-008 -5.3587e-007 (norm 2.62e-004) Successive criterion values within tolerance (1e-008) Probit, using observations 1-296 Dependent variable: external Standard errors based on Hessian coefficient std. error z p-value ----------------------------------------------------------- const 0.301095 1.52224 0.1978 0.8432 POT1 7.25456 1.06883 6.787 1.14e-011 *** TAILLE 0.0204915 0.0762379 0.2688 0.7881 INV_ANT −0.294660 0.540840 −0.5448 0.5859 CF_ANT 1.88555 1.24252 1.518 0.1291 VOLAT 3.48830 5.31192 0.6567 0.5114 D_DIV −0.257232 0.298702 −0.8612 0.3891 SCOREZ −0.318773 0.290522 −1.097 0.2725 D_V −0.314914 0.928867 −0.3390 0.7346 DRD −2.54478 2.92467 −0.8701 0.3842 MTB 0.592223 0.116229 5.095 3.48e-07 *** RFR −0.0191340 0.186414 −0.1026 0.9182 TANGI −0.0618008 0.882095 −0.07006 0.9441 AGE_BASE −0.0335791 0.0210625 −1.594 0.1109 REND −0.279357 0.260663 −1.072 0.2838 TAXR −0.213764 0.558307 −0.3829 0.7018 DFC_IND2 −1.58790 3.54576 −0.4478 0.6543 PROFI −2.98678 2.82992 −1.055 0.2912 SIC2DIGIT 0.0698219 0.0741635 0.9415 0.3465 Mean dependent var 0.277027 S.D. dependent var 0.448288 McFadden R-squared 0.336201 Adjusted R-squared 0.227429 Log-likelihood −115.9501 Akaike criterion 269.9001 Schwarz criterion 340.0170 Hannan-Quinn 297.9735 Test for normality of residual - Null hypothesis: error is normally distributed Test statistic: Chi-square(2) = 13.6994 with p-value = 0.00105975 Iteration 1: loglikelihood = -52.5842572593 (steplength = 1) Parameters: -0.33466 0.67198 -0.037006 0.28296 -0.13922 3.3417 -0.22620 0.0067796 0.33270 1.1143 0.068529 0.020298 -0.52366 0.0032300 0.080058 -0.019956 -1.7180 -2.7484 0.0036713 Gradients: -260.48 -36.570 -3038.2 -57.849 -57.115 -6.1820 -199.97 -380.57 -62.295 -6.8759 -497.01 -413.79 -64.421 -5102.1 -35.551 -95.161 -56.419 -21.829 -1266.9 (norm 5.64e+000) Iteration 2: loglikelihood = -30.1033151997 (steplength = 1) Parameters: -0.17922 1.1701 -0.072678 0.58465 -0.29499 6.7094 -0.37795 -0.031348 0.60270 1.8059 0.14008 0.030686 -0.98308 0.0071969 0.11724 -0.072590 -2.9877 -5.3662 0.00020404 Gradients: -55.084 -7.0777 -657.34 -10.432 -12.417 -0.96932 -47.445 -80.946 -12.842 -1.2957 -88.789 -83.364 -13.896 -1114.2 -6.1851 -20.314 -12.043 -4.7960 -259.32 (norm 3.35e+000) Iteration 3: loglikelihood = -24.2667468363 (steplength = 1) Parameters: 0.66757 1.9202 -0.12713 0.99906 -0.22534 11.187 -0.50592 -0.15831 0.84673 2.7027 0.18765 -0.016966 -2.0562 0.013852 0.11876 -0.14206 -4.1577 -8.3711 -0.018341 Gradients: -15.627 -1.3144 -187.70 -2.9809 -3.6887 -0.26479 -14.026 -23.619 -3.5582 -0.38110 -26.835 -24.150 -4.1988 -315.78 -1.5874 -5.7265 -3.4313 -1.4402 -73.094 (norm 2.36e+000) Iteration 4: loglikelihood = -22.0276590435 (steplength = 1) Parameters: 2.1302 2.6462 -0.21004 1.4851 -0.34673 16.065 -0.58835 -0.39991 1.0499 2.9902 0.23599 -0.11412 -3.5780 0.023943 0.075039 -0.23842 -4.7233 -11.663 -0.049490 Gradients: -5.2007 -0.27545 -62.433 -1.0920 -1.3456 -0.098099 -4.6427 -8.1640 -1.1711 -0.15334 -10.054 -8.3032 -1.5164 -103.99 -0.57669 -1.9117 -1.1382 -0.51464 -24.954 (norm 1.79e+000) Iteration 5: loglikelihood = -21.0928432770 (steplength = 1) Parameters: 3.9237 3.3492 -0.32264 1.9777 -0.75273 20.118 -0.67820 -0.65919 1.2795 2.4560 0.30665 -0.24759 -5.1936 0.038016 0.035465 -0.41013 -4.6112 -15.422 -0.080900 Gradients: -2.0334 -0.13717 -24.494 -0.48961 -0.58114 -0.048747 -1.7864 -3.1746 -0.47428 -0.077046 -4.2563 -3.3198 -0.61547 -40.653 -0.22207 -0.77623 -0.43943 -0.21192 -9.9977 (norm 1.38e+000) Iteration 6: loglikelihood = -20.8350870121 (steplength = 1) Parameters: 5.3429 3.9341 -0.42036 2.3958 -1.2236 22.253 -0.78213 -0.81177 1.4900 2.0915 0.39045 -0.38835 -6.4703 0.051814 0.026962 -0.53752 -4.3793 -18.290 -0.11030 Gradients: -0.86424 -0.098145 -10.321 -0.18448 -0.22136 -0.026769 -0.70758 -1.2790 -0.20363 -0.029711 -1.4285 -1.5006 -0.24246 -16.805 -0.039403 -0.33230 -0.18690 -0.077626 -3.9892 (norm 1.00e+000) Iteration 7: loglikelihood = -20.8091107568 (steplength = 1) Parameters: 5.9131 4.1716 -0.46212 2.5624 -1.3641 23.086 -0.83520 -0.86663 1.5884 1.9569 0.42768 -0.45973 -7.0069 0.058617 0.031473 -0.57980 -4.2216 -19.433 -0.12573 Gradients: -0.30435 -0.050855 -3.4799 -0.056347 -0.057021 -0.010394 -0.20889 -0.44343 -0.073814 -0.0086059 -0.41127 -0.58192 -0.073279 -5.4520 0.0010671 -0.10905 -0.066156 -0.022316 -1.3819 (norm 6.07e-001) Iteration 8: loglikelihood = -20.8087618346 (steplength = 1) Parameters: 5.9855 4.2013 -0.46749 2.5830 -1.3767 23.203 -0.84261 -0.87368 1.6007 1.9479 0.43228 -0.46963 -7.0749 0.059549 0.032517 -0.58474 -4.1973 -19.576 -0.12801 Gradients: -0.039819 -0.0074390 -0.44536 -0.0069189 -0.0062802 -0.0012307 -0.025340 -0.057498 -0.0099439 -0.00093631 -0.053767 -0.078247 -0.0083026 -0.68788 2.1219e-005 -0.014077 -0.0086341 -0.0027223 -0.18848 (norm 2.18e-001) Iteration 9: loglikelihood = -20.8087617622 (steplength = 1) Parameters: 5.9866 4.2018 -0.46757 2.5832 -1.3769 23.205 -0.84272 -0.87378 1.6008 1.9479 0.43235 -0.46977 -7.0759 0.059563 0.032533 -0.58481 -4.1970 -19.578 -0.12805 Gradients: -0.00060491 -0.00011343 -0.0067224 -0.00010350-9.2711e-005-1.6785e-005 -0.00037826 -0.00087400 -0.00015303-1.2306e-005 -0.00082900 -0.0011871 -0.00011664 -0.010342-6.8660e-006 -0.00021166 -0.00013087-4.0205e-005 -0.0029613 (norm 2.67e-002) Iteration 10: loglikelihood = -20.8087617622 (steplength = 0.5) Parameters: 5.9866 4.2018 -0.46757 2.5832 -1.3769 23.205 -0.84272 -0.87378 1.6008 1.9479 0.43235 -0.46977 -7.0759 0.059563 0.032533 -0.58481 -4.1970 -19.578 -0.12805 Gradients: -1.2852e-007-2.4103e-008-1.4239e-006-2.2257e-008-1.9628e-008-3.3888e-009 -8.0191e-008-1.8715e-007-3.2699e-008-2.4207e-009-1.7654e-007-2.5280e-007 -2.4095e-008-2.1837e-006-2.4492e-009-4.4818e-008-2.7784e-008-8.4394e-009 -6.4220e-007 (norm 3.89e-004) --- FINAL VALUES: loglikelihood = -20.8087617622 (steplength = 0.5) Parameters: 5.9866 4.2018 -0.46757 2.5832 -1.3769 23.205 -0.84272 -0.87378 1.6008 1.9479 0.43235 -0.46977 -7.0759 0.059563 0.032533 -0.58481 -4.1970 -19.578 -0.12805 Gradients: -6.4262e-008-1.2052e-008-7.1197e-007-1.1129e-008-9.8139e-009-1.6944e-009 -4.0096e-008-9.3574e-008-1.6349e-008-1.2104e-009-8.8268e-008-1.2640e-007 -1.2048e-008-1.0918e-006-1.2246e-009-2.2409e-008-1.3892e-008-4.2197e-009 -3.2110e-007 (norm 2.75e-004) Successive criterion values within tolerance (1e-008) Probit, using observations 1-296 Dependent variable: equity Standard errors based on Hessian coefficient std. error z p-value --------------------------------------------------------- const 5.98658 4.67475 1.281 0.2003 POT2 4.20176 1.57041 2.676 0.0075 *** TAILLE −0.467572 0.307759 −1.519 0.1287 INV_ANT 2.58325 1.30845 1.974 0.0484 ** CF_ANT −1.37685 2.43441 −0.5656 0.5717 VOLAT 23.2046 10.2761 2.258 0.0239 ** D_DIV −0.842718 0.715602 −1.178 0.2389 SCOREZ −0.873782 0.931908 −0.9376 0.3484 D_V 1.60082 2.12154 0.7546 0.4505 DRD 1.94793 7.88598 0.2470 0.8049 MTB 0.432349 0.269034 1.607 0.1080 RFR −0.469774 0.536119 −0.8763 0.3809 TANGI −7.07592 3.54591 −1.996 0.0460 ** AGE_BASE 0.0595628 0.0695811 0.8560 0.3920 REND 0.0325328 0.607229 0.05358 0.9573 TAXR −0.584806 1.40269 −0.4169 0.6767 DFC_IND2 −4.19696 8.67950 −0.4835 0.6287 PROFI −19.5779 8.88441 −2.204 0.0276 ** SIC2DIGIT −0.128049 0.207834 −0.6161 0.5378 Mean dependent var 0.037162 S.D. dependent var 0.189479 McFadden R-squared 0.557356 Adjusted R-squared 0.153188 Log-likelihood −20.80876 Akaike criterion 79.61752 Schwarz criterion 149.7344 Hannan-Quinn 107.6909 Test for normality of residual - Null hypothesis: error is normally distributed Test statistic: Chi-square(2) = 61.7921 with p-value = 3.81953e-014 Iteration 1: loglikelihood = -130.787148830 (steplength = 1) Parameters: 0.031968 7.1160 0.039171 -0.21914 1.9315 1.8278 -0.31693 -0.31342 -0.27102 -2.8971 0.58328 -0.019506 -0.051445 -0.035786 -0.24773 -0.12462 -1.2948 -2.6384 0.068354 4.9968 3.7298 -0.34002 2.7074 -0.55119 19.516 -1.3326 -1.3491 1.5925 -5.1513 0.29853 -0.38265 -5.7361 0.041642 0.14496 -0.72172 -1.2335 -20.106 -0.097534 1.1582 Gradients: -1.5367e-007 8.2990e-008-1.7666e-006-1.6409e-008-3.1435e-008-5.1518e-009 -1.2367e-007-3.0499e-007-4.4339e-008-7.3258e-009-2.0242e-007-3.0174e-007 -1.6471e-008-2.9471e-006-2.5526e-008-3.0580e-008-3.2561e-008-1.6313e-008 -5.3234e-007-6.4079e-008-1.1971e-008-7.0997e-007-1.1097e-008-9.7771e-009 -1.6897e-009-4.0013e-008-9.3349e-008-1.6302e-008-1.2097e-009-8.7972e-008 -1.2607e-007-1.2016e-008-1.0885e-006-1.1795e-009-2.2347e-008-1.3852e-008 -4.2035e-009-3.2030e-007 10.107 (norm 5.48e-001) Iteration 2: loglikelihood = -129.335414642 (steplength = 1) Parameters: 0.099837 7.0956 0.034049 -0.23373 2.0237 1.7496 -0.28973 -0.29411 -0.27097 -2.7166 0.57080 -0.015073 -0.088824 -0.034477 -0.24414 -0.14115 -1.4400 -2.7609 0.062613 6.9296 4.3261 -0.46778 2.9963 -1.0352 21.390 -1.2688 -1.5269 1.2692 -3.8577 0.38035 -0.50095 -7.0942 0.068132 0.039101 -0.95691 -1.8891 -22.183 -0.12754 1.7761 Gradients: 0.099127 0.053624 1.2915 -0.063547 0.018422 0.010023 0.48207 0.077144 -0.13227 -0.048258 0.071754 0.73625 0.13614 3.9993 0.049949 -0.011269 0.039489 0.010466 -2.2003 2.2812 0.70051 21.875 0.47967 0.47742 0.077409 1.5194 3.9359 0.81009 0.15891 3.8272 3.2998 0.20330 42.777 -0.065361 0.80311 0.42448 0.18201 14.565 2.6229 (norm 1.27e+000) Iteration 3: loglikelihood = -128.903327145 (steplength = 1) Parameters: 0.13616 7.1136 0.033793 -0.26184 2.0819 1.2271 -0.28933 -0.27933 -0.23812 -2.4925 0.56700 -0.021535 -0.091844 -0.035046 -0.24700 -0.13708 -1.5904 -2.8106 0.063020 6.9003 4.2393 -0.45166 2.9852 -1.2219 21.092 -1.3171 -1.5043 1.0100 -4.3588 0.38059 -0.45024 -6.9371 0.071281 0.066235 -0.85429 -3.0193 -21.354 -0.13661 2.5276 Gradients: 0.45370 -0.024633 4.1177 -0.025239 0.079541 0.0077866 0.21036 0.80788 0.22311 0.056710 0.22088 0.44796 -0.0095258 6.8164 -0.18614 0.18612 0.083636 0.029139 3.4429 -0.57360 -0.24609 -6.2892 -0.0089408 -0.12528 -0.00094623 -0.30671 -0.75457 -0.20794 -0.025518 -0.12152 -0.58079 -0.031878 -10.667 0.051376 -0.22078 -0.11979 -0.032502 -3.1939 0.91562 (norm 6.62e-001) Iteration 4: loglikelihood = -128.683022741 (steplength = 1) Parameters: 0.11831 7.1286 0.034864 -0.26368 2.0998 0.92689 -0.29226 -0.28181 -0.25394 -2.5081 0.56945 -0.020506 -0.091600 -0.035423 -0.24784 -0.12725 -1.5255 -2.7766 0.062635 6.8917 4.2914 -0.45026 2.9721 -1.2123 21.661 -1.2954 -1.4678 0.93555 -3.8431 0.38492 -0.44680 -7.0336 0.073234 0.061223 -0.84182 -3.4461 -21.417 -0.14103 3.2694 Gradients: -0.60124 0.13181 -3.8519 0.036287 -0.17099 -0.0037274 0.0037505 -1.6802 -0.44299 -0.15391 0.94057 -0.42560 0.047069 -5.6429 0.43499 -0.29145 -0.096033 -0.022574 -6.0473 0.59713 0.12474 3.5224 -0.035815 0.17177 0.0089871 -0.011325 1.7463 0.46029 0.16328 -0.77309 0.44581 -0.046883 4.7813 -0.49176 0.28085 0.095682 0.026031 6.2253 0.44124 (norm 6.98e-001) Iteration 5: loglikelihood = -128.621333519 (steplength = 0.5) Parameters: 0.11507 7.1277 0.035293 -0.26571 2.1044 0.82657 -0.29404 -0.27748 -0.24541 -2.4597 0.56785 -0.022326 -0.087657 -0.035554 -0.24732 -0.12339 -1.5409 -2.7632 0.062484 6.9352 4.2864 -0.45803 2.9611 -1.2008 21.844 -1.2768 -1.4996 0.93727 -4.2793 0.38092 -0.43315 -7.0144 0.074870 0.062418 -0.88175 -3.4072 -21.451 -0.13403 3.8183 Gradients: 0.47589 -0.10597 4.0241 0.057730 0.11892 0.015059 0.10523 1.0050 0.24478 0.077913 0.24489 0.57507 0.026595 6.4902 -0.16782 0.22980 0.086153 0.025351 3.7838 -0.44130 -0.098045 -3.8472 -0.047351 -0.10644 -0.011675 -0.10418 -0.90830 -0.21906 -0.068731 -0.085353 -0.51022 -0.020581 -6.4111 0.12576 -0.22043 -0.079245 -0.022635 -3.4496 0.11438 (norm 5.81e-001) Iteration 6: loglikelihood = -128.573640541 (steplength = 1) Parameters: 0.11330 7.1327 0.035774 -0.26505 2.0974 0.82303 -0.29698 -0.27368 -0.23736 -2.4369 0.56690 -0.023839 -0.080888 -0.035602 -0.24653 -0.12221 -1.5646 -2.7463 0.062276 6.9551 4.2954 -0.46339 2.9573 -1.1787 22.073 -1.2524 -1.5209 0.94481 -4.2174 0.37906 -0.42841 -7.0479 0.074942 0.059161 -0.90156 -3.2731 -21.548 -0.12911 4.6216 Gradients: -0.38635 0.12178 -2.2717 -0.0079471 -0.12927 -0.0043520 0.081539 -1.0891 -0.29837 -0.10610 0.22262 -0.31238 0.012434 -2.5962 0.33045 -0.18935 -0.063744 -0.012831 -4.0683 0.40962 0.034778 2.3937 0.013515 0.13686 0.0065716 -0.079457 1.1545 0.31581 0.11226 -0.12441 0.35417 -0.0088784 2.6536 -0.35928 0.19538 0.068416 0.014674 4.2873 0.095297 (norm 5.53e-001) Iteration 7: loglikelihood = -128.573168148 (steplength = 1) Parameters: 0.11106 7.1337 0.036003 -0.26526 2.0972 0.79169 -0.29737 -0.27222 -0.23607 -2.4169 0.56650 -0.025388 -0.076285 -0.035664 -0.24659 -0.12057 -1.5708 -2.7313 0.062257 6.9611 4.2952 -0.46571 2.9490 -1.1705 22.101 -1.2450 -1.5274 0.95407 -4.4303 0.37777 -0.41940 -7.0498 0.075415 0.059131 -0.90942 -3.2773 -21.581 -0.12672 5.6809 Gradients: 0.31983 -0.12261 2.5860 0.093956 0.10450 0.017622 0.077328 0.64927 0.15837 0.049508 0.84309 0.54671 0.049391 3.8254 -0.15591 0.13576 0.061462 0.017669 2.4648 -0.31532 -0.010821 -2.5646 -0.093631 -0.10348 -0.017098 -0.075960 -0.63511 -0.15441 -0.048091 -0.82255 -0.53945 -0.048729 -3.8311 0.14786 -0.13519 -0.060498 -0.017289 -2.4255 0.029588 (norm 5.21e-001) Iteration 8: loglikelihood = -128.559756680 (steplength = 0.5) Parameters: 0.10620 7.1392 0.036406 -0.26531 2.0956 0.77730 -0.29777 -0.27378 -0.24421 -2.4417 0.56698 -0.024546 -0.077167 -0.035676 -0.24556 -0.11901 -1.5607 -2.7071 0.062121 6.9724 4.3073 -0.46771 2.9571 -1.1761 22.198 -1.2396 -1.5322 0.95658 -4.2918 0.37880 -0.42292 -7.0763 0.075463 0.058072 -0.91423 -3.1883 -21.660 -0.12653 7.2337 Gradients: -1.5977 0.48173 -10.062 -0.067709 -0.50819 -0.029188 0.26829 -4.4020 -1.1836 -0.42192 0.52663 -1.1991 0.015348 -12.748 1.2331 -0.77011 -0.26512 -0.061259 -16.406 1.6062 0.16744 10.116 0.068816 0.51058 0.029712 -0.26731 4.4242 1.1895 0.42389 -0.50510 1.2119 -0.014455 12.796 -1.2417 0.77272 0.26680 0.061742 16.478 0.0099495 (norm 1.08e+000) Iteration 9: loglikelihood = -128.549850444 (steplength = 0.00195313) Parameters: 0.10624 7.1388 0.036401 -0.26529 2.0955 0.77724 -0.29771 -0.27373 -0.24402 -2.4411 0.56698 -0.024574 -0.077009 -0.035676 -0.24558 -0.11906 -1.5610 -2.7076 0.062120 6.9752 4.3032 -0.46815 2.9537 -1.1742 22.187 -1.2389 -1.5345 0.95747 -4.3942 0.37812 -0.42076 -7.0661 0.075624 0.058599 -0.91661 -3.2027 -21.645 -0.12588 7.2369 Gradients: 1.7241 -0.66642 16.004 0.64175 0.48658 0.11929 0.87549 2.7294 0.59518 0.13176 6.6649 4.0825 0.38580 24.531 -0.50644 0.62561 0.36631 0.10865 9.8272 -1.6928 -0.059440 -15.787 -0.63722 -0.47703 -0.11798 -0.87422 -2.6519 -0.57489 -0.12497 -6.6202 -4.0417 -0.38344 -24.263 0.48322 -0.61283 -0.36054 -0.10715 -9.5501 0.0017564 (norm 1.24e+000) Iteration 10: loglikelihood = -128.545745347 (steplength = 0.000976563) Parameters: 0.10621 7.1386 0.036402 -0.26529 2.0954 0.77714 -0.29771 -0.27371 -0.24390 -2.4410 0.56697 -0.024568 -0.076960 -0.035677 -0.24559 -0.11910 -1.5611 -2.7079 0.062120 6.9779 4.3014 -0.46848 2.9524 -1.1736 22.185 -1.2384 -1.5359 0.95790 -4.4389 0.37783 -0.41997 -7.0618 0.075710 0.058825 -0.91788 -3.2071 -21.640 -0.12557 7.2393 Gradients: 1.7214 -0.66525 15.980 0.64065 0.48572 0.11906 0.87378 2.7250 0.59419 0.13150 6.6525 4.0766 0.38509 24.492 -0.50519 0.62453 0.36573 0.10848 9.8106 -1.6901 -0.059517 -15.764 -0.63613 -0.47618 -0.11776 -0.87252 -2.6477 -0.57394 -0.12473 -6.6079 -4.0359 -0.38274 -24.225 0.48202 -0.61178 -0.35998 -0.10698 -9.5341 0.0017556 (norm 1.23e+000) Iteration 11: loglikelihood = -128.543273538 (steplength = 0.5) Parameters: 0.10798 7.1370 0.036216 -0.26532 2.0963 0.78514 -0.29740 -0.27300 -0.24042 -2.4280 0.56672 -0.025082 -0.076168 -0.035674 -0.24610 -0.11964 -1.5648 -2.7172 0.062217 6.9685 4.2982 -0.46597 2.9504 -1.1749 22.116 -1.2446 -1.5272 0.95041 -4.4034 0.37846 -0.41947 -7.0620 0.075455 0.058748 -0.90673 -3.2823 -21.595 -0.12734 8.0129 Gradients: 1.3680 -0.55396 13.424 0.59374 0.37690 0.10871 0.87292 1.8730 0.37350 0.057283 6.4066 3.6590 0.36754 20.952 -0.29367 0.46123 0.30324 0.093000 6.6342 -1.3367 -0.027656 -13.208 -0.58921 -0.36735 -0.10741 -0.87166 -1.7955 -0.35321 -0.050498 -6.3620 -3.6182 -0.36518 -20.684 0.27047 -0.44844 -0.29748 -0.091495 -6.3572 0.0025729 (norm 1.12e+000) Iteration 12: loglikelihood = -128.542712919 (steplength = 0.125) Parameters: 0.10917 7.1355 0.036242 -0.26523 2.0959 0.77986 -0.29790 -0.27285 -0.24006 -2.4273 0.56666 -0.024994 -0.076919 -0.035671 -0.24598 -0.11955 -1.5685 -2.7178 0.062140 6.9794 4.3009 -0.46826 2.9524 -1.1736 22.168 -1.2397 -1.5344 0.95855 -4.4135 0.37793 -0.42097 -7.0605 0.075646 0.058798 -0.91563 -3.2166 -21.638 -0.12588 8.1291 Gradients: -0.45797 0.16298 -3.0161 -0.063352 -0.15066 -0.018018 -0.011231 -1.1667 -0.30843 -0.10665 -0.49887 -0.49869 -0.030019 -3.9864 0.36084 -0.20085 -0.082196 -0.021017 -4.3467 0.47099 0.027723 3.0888 0.065713 0.15540 0.018729 0.0099856 1.2036 0.31836 0.11007 0.52265 0.51532 0.031246 4.0546 -0.37450 0.20509 0.084671 0.021714 4.4793 0.0011106 (norm 6.01e-001) Iteration 13: loglikelihood = -128.542617385 (steplength = 0.125) Parameters: 0.10968 7.1355 0.036187 -0.26519 2.0957 0.78094 -0.29761 -0.27280 -0.24006 -2.4245 0.56667 -0.025280 -0.076479 -0.035668 -0.24615 -0.11940 -1.5687 -2.7181 0.062147 6.9621 4.2995 -0.46666 2.9504 -1.1707 22.135 -1.2418 -1.5295 0.95814 -4.4188 0.37789 -0.41891 -7.0586 0.075464 0.058854 -0.91268 -3.2412 -21.615 -0.12613 8.2563 Gradients: -0.61847 0.17894 -5.3572 -0.079900 -0.14918 -0.016881 -0.13361 -1.2194 -0.30089 -0.093044 -0.65501 -0.88574 -0.039740 -8.0666 0.19894 -0.31017 -0.11063 -0.026409 -4.3969 0.62890 0.047577 5.4096 0.081776 0.15320 0.017492 0.13230 1.2505 0.30936 0.096028 0.67485 0.89761 0.040741 8.1101 -0.21131 0.31352 0.11260 0.026964 4.5095 0.00099238 (norm 6.78e-001) Iteration 14: loglikelihood = -128.542451799 (steplength = 0.125) Parameters: 0.10912 7.1351 0.036240 -0.26525 2.0962 0.77973 -0.29800 -0.27262 -0.23907 -2.4251 0.56658 -0.025016 -0.076714 -0.035676 -0.24602 -0.11978 -1.5693 -2.7193 0.062161 6.9812 4.3012 -0.46839 2.9526 -1.1738 22.171 -1.2396 -1.5347 0.95864 -4.4065 0.37796 -0.42120 -7.0613 0.075656 0.058726 -0.91561 -3.2152 -21.641 -0.12590 8.3751 Gradients: -0.42136 0.13112 -3.3312 -0.042671 -0.11423 -0.012949 -0.10205 -0.89253 -0.23173 -0.080518 -0.43963 -0.41345 -0.025126 -5.2438 0.21755 -0.23556 -0.071521 -0.015488 -3.3717 0.42966 0.011602 3.3677 0.044114 0.11765 0.013473 0.10089 0.91846 0.23892 0.083129 0.45620 0.42113 0.025939 5.2703 -0.22870 0.23830 0.073066 0.015917 3.4669 0.00088092 (norm 5.61e-001) Iteration 15: loglikelihood = -128.542352398 (steplength = 0.125) Parameters: 0.10966 7.1351 0.036185 -0.26521 2.0959 0.78080 -0.29770 -0.27260 -0.23920 -2.4226 0.56660 -0.025302 -0.076304 -0.035672 -0.24619 -0.11960 -1.5694 -2.7195 0.062164 6.9635 4.2998 -0.46679 2.9506 -1.1708 22.138 -1.2416 -1.5298 0.95845 -4.4134 0.37791 -0.41910 -7.0592 0.075472 0.058799 -0.91284 -3.2389 -21.618 -0.12611 8.4918 Gradients: -0.45991 0.13269 -3.9555 -0.059190 -0.11124 -0.012219 -0.084099 -0.92411 -0.22795 -0.069876 -0.45265 -0.67658 -0.027885 -5.8366 0.15054 -0.22345 -0.082805 -0.020148 -3.3068 0.46627 0.039723 3.9773 0.060234 0.11411 0.012666 0.083071 0.94542 0.23400 0.072161 0.46629 0.68035 0.028528 5.8471 -0.16065 0.22565 0.083960 0.020461 3.3865 0.00077916 (norm 5.83e-001) Iteration 16: loglikelihood = -128.542304812 (steplength = 0.125) Parameters: 0.10910 7.1347 0.036238 -0.26527 2.0964 0.77961 -0.29808 -0.27245 -0.23832 -2.4233 0.56652 -0.025035 -0.076562 -0.035679 -0.24606 -0.11996 -1.5699 -2.7205 0.062176 6.9823 4.3014 -0.46851 2.9528 -1.1739 22.173 -1.2394 -1.5349 0.95895 -4.4020 0.37797 -0.42136 -7.0617 0.075663 0.058679 -0.91578 -3.2130 -21.643 -0.12587 8.6071 Gradients: -0.29548 0.093183 -2.2578 -0.025480 -0.082472 -0.0090357 -0.069110 -0.64529 -0.17008 -0.060798 -0.28033 -0.24602 -0.015709 -3.5820 0.17102 -0.16991 -0.049203 -0.010287 -2.4613 0.30037 0.0045095 2.2693 0.026228 0.084891 0.0094180 0.068202 0.66289 0.17518 0.062797 0.29163 0.24710 0.016219 3.5816 -0.18013 0.17168 0.050067 0.010513 2.5284 0.00068911 (norm 4.68e-001) Iteration 17: loglikelihood = -128.542165497 (steplength = 0.125) Parameters: 0.10965 7.1348 0.036183 -0.26522 2.0961 0.78069 -0.29777 -0.27245 -0.23854 -2.4211 0.56655 -0.025319 -0.076173 -0.035675 -0.24622 -0.11975 -1.5700 -2.7205 0.062177 6.9644 4.3000 -0.46690 2.9507 -1.1708 22.140 -1.2414 -1.5300 0.95875 -4.4096 0.37791 -0.41923 -7.0595 0.075478 0.058759 -0.91300 -3.2368 -21.621 -0.12608 8.7219 Gradients: -0.35305 0.10039 -3.0481 -0.044579 -0.084133 -0.0088842 -0.056721 -0.71302 -0.17525 -0.052999 -0.31801 -0.53461 -0.019918 -4.4357 0.11027 -0.16797 -0.063847 -0.015717 -2.5296 0.35632 0.033578 3.0466 0.044986 0.086113 0.0092053 0.055918 0.72685 0.17944 0.054748 0.32693 0.53219 0.020286 4.4211 -0.11864 0.16929 0.064379 0.015844 2.5842 0.00060959 (norm 5.09e-001) Iteration 18: loglikelihood = -128.542020137 (steplength = 0.0625) Parameters: 0.10937 7.1346 0.036210 -0.26525 2.0963 0.78011 -0.29795 -0.27238 -0.23814 -2.4216 0.56651 -0.025185 -0.076311 -0.035679 -0.24615 -0.11992 -1.5702 -2.7210 0.062182 6.9738 4.3008 -0.46775 2.9518 -1.1724 22.158 -1.2403 -1.5326 0.95899 -4.4041 0.37794 -0.42035 -7.0608 0.075573 0.058701 -0.91447 -3.2240 -21.633 -0.12596 8.7792 Gradients: -0.20435 0.065582 -1.4860 -0.012987 -0.059274 -0.0061822 -0.046089 -0.46462 -0.12494 -0.046337 -0.16567 -0.12489 -0.0089183 -2.3927 0.13627 -0.12275 -0.033024 -0.0064967 -1.7962 0.20664 -0.00075319 1.4783 0.013204 0.060931 0.0064558 0.045378 0.47584 0.12845 0.047867 0.17293 0.12092 0.0091961 2.3719 -0.14382 0.12378 0.033366 0.0065683 1.8416 0.00053934 (norm 3.87e-001) Iteration 19: loglikelihood = -128.541970359 (steplength = 0.03125) Parameters: 0.10950 7.1346 0.036197 -0.26523 2.0962 0.78036 -0.29788 -0.27239 -0.23820 -2.4210 0.56652 -0.025251 -0.076221 -0.035678 -0.24619 -0.11987 -1.5702 -2.7210 0.062182 6.9696 4.3005 -0.46737 2.9513 -1.1717 22.150 -1.2408 -1.5314 0.95894 -4.4060 0.37793 -0.41985 -7.0603 0.075530 0.058721 -0.91382 -3.2296 -21.628 -0.12601 8.8079 Gradients: -0.29070 0.081909 -2.5136 -0.036011 -0.068643 -0.0070368 -0.042444 -0.58964 -0.14470 -0.043453 -0.24466 -0.44651 -0.015500 -3.6257 0.088548 -0.13665 -0.052675 -0.013045 -2.0811 0.29257 0.029156 2.5033 0.036148 0.070160 0.0072897 0.041775 0.59973 0.14791 0.044887 0.25121 0.44187 0.015740 3.6024 -0.095730 0.13755 0.052934 0.013092 2.1226 0.00050701 (norm 4.60e-001) Iteration 20: loglikelihood = -128.541779875 (steplength = 1) Parameters: 0.11000 7.1342 0.036141 -0.26535 2.0968 0.78149 -0.29780 -0.27195 -0.23627 -2.4154 0.56644 -0.025561 -0.075461 -0.035685 -0.24644 -0.12024 -1.5719 -2.7248 0.062214 6.9756 4.3016 -0.46797 2.9523 -1.1727 22.163 -1.2401 -1.5329 0.96023 -4.3932 0.37803 -0.42080 -7.0622 0.075578 0.058539 -0.91475 -3.2187 -21.640 -0.12594 9.7820 Gradients: -0.18535 0.059345 -1.3468 -0.011498 -0.053687 -0.0055460 -0.040928 -0.42238 -0.11359 -0.042103 -0.14584 -0.11329 -0.0078459 -2.1634 0.12359 -0.11118 -0.029946 -0.0058956 -1.6312 0.18707 -0.00051947 1.3357 0.011607 0.055143 0.0057897 0.040280 0.43201 0.11667 0.043492 0.15209 0.10848 0.0080705 2.1394 -0.13058 0.11203 0.030177 0.0059352 1.6711 0.00049404 (norm 3.68e-001) Iteration 21: loglikelihood = -128.541706611 (steplength = 0.03125) Parameters: 0.11011 7.1342 0.036131 -0.26534 2.0967 0.78170 -0.29773 -0.27197 -0.23639 -2.4151 0.56645 -0.025615 -0.075399 -0.035684 -0.24647 -0.12018 -1.5718 -2.7247 0.062213 6.9713 4.3013 -0.46759 2.9518 -1.1720 22.155 -1.2406 -1.5317 0.96015 -4.3954 0.37801 -0.42029 -7.0617 0.075534 0.058564 -0.91410 -3.2245 -21.634 -0.12599 9.8113 Gradients: 0.74405 -0.29787 8.3385 0.37979 0.18493 0.065213 0.68026 0.63103 0.081093 -0.018833 4.2524 2.2215 0.24406 14.172 0.037825 0.28453 0.16882 0.050728 2.2856 -0.74433 0.011382 -8.3418 -0.37985 -0.18496 -0.065211 -0.68025 -0.63142 -0.081153 0.018839 -4.2523 -2.2224 -0.24407 -14.176 -0.038006 -0.28461 -0.16888 -0.050746 -2.2867 0.00024158 (norm 8.55e-001) Iteration 22: loglikelihood = -128.541659105 (steplength = 1) Parameters: 0.11004 7.1341 0.036142 -0.26535 2.0968 0.78153 -0.29782 -0.27196 -0.23627 -2.4155 0.56644 -0.025546 -0.075516 -0.035685 -0.24643 -0.12025 -1.5720 -2.7249 0.062213 6.9761 4.3016 -0.46805 2.9524 -1.1727 22.164 -1.2400 -1.5331 0.96051 -4.3938 0.37801 -0.42089 -7.0620 0.075584 0.058554 -0.91504 -3.2166 -21.641 -0.12589 10.796 Gradients: 0.81869 -0.30872 9.1855 0.39152 0.19204 0.064402 0.65837 0.76310 0.10566 -0.018282 4.2082 2.4777 0.24352 15.097 0.075598 0.29695 0.18499 0.056010 2.6052 -0.81900 -0.018546 -9.1892 -0.39159 -0.19208 -0.064400 -0.65836 -0.76354 -0.10573 0.018287 -4.2082 -2.4787 -0.24353 -15.102 -0.075793 -0.29704 -0.18506 -0.056030 -2.6064 0.00023440 (norm 8.81e-001) Iteration 23: loglikelihood = -128.541648112 (steplength = 0.00195313) Parameters: 0.11000 7.1342 0.036141 -0.26535 2.0968 0.78171 -0.29781 -0.27196 -0.23626 -2.4155 0.56644 -0.025541 -0.075506 -0.035685 -0.24643 -0.12025 -1.5719 -2.7249 0.062215 6.9764 4.3015 -0.46798 2.9524 -1.1730 22.162 -1.2402 -1.5329 0.95992 -4.3923 0.37805 -0.42091 -7.0623 0.075581 0.058541 -0.91454 -3.2200 -21.639 -0.12599 10.799 Gradients: -0.66057 0.18957 -6.3936 -0.17320 -0.17249 -0.024307 -0.24821 -1.0341 -0.23123 -0.061239 -1.3729 -1.1901 -0.089213 -10.657 0.055495 -0.34390 -0.12285 -0.029773 -4.0017 0.66021 0.041624 6.3892 0.17314 0.17245 0.024308 0.24821 1.0335 0.23114 0.061242 1.3729 1.1889 0.089204 10.651 -0.055700 0.34380 0.12278 0.029749 4.0003 5.2230e-005 (norm 7.19e-001) Iteration 24: loglikelihood = -128.541624532 (steplength = 0.015625) Parameters: 0.11020 7.1341 0.036134 -0.26533 2.0967 0.78146 -0.29779 -0.27197 -0.23638 -2.4153 0.56645 -0.025584 -0.075518 -0.035683 -0.24645 -0.12018 -1.5721 -2.7248 0.062207 6.9736 4.3015 -0.46784 2.9521 -1.1722 22.160 -1.2402 -1.5325 0.96053 -4.3953 0.37799 -0.42059 -7.0617 0.075560 0.058568 -0.91473 -3.2194 -21.638 -0.12591 10.819 Gradients: -0.52319 0.16888 -4.5923 -0.16986 -0.16503 -0.023949 -0.11163 -0.96734 -0.22299 -0.060240 -1.2090 -1.0429 -0.080935 -7.0978 0.13501 -0.21844 -0.10267 -0.028113 -3.7216 0.52268 0.048188 4.5861 0.16980 0.16498 0.023951 0.11149 0.96674 0.22289 0.060242 1.2089 1.0416 0.080917 7.0884 -0.13529 0.21820 0.10257 0.028088 3.7200 5.2119e-005 (norm 6.56e-001) Iteration 25: loglikelihood = -128.541618858 (steplength = 0.00390625) Parameters: 0.11009 7.1342 0.036133 -0.26534 2.0967 0.78192 -0.29777 -0.27198 -0.23637 -2.4153 0.56645 -0.025568 -0.075494 -0.035683 -0.24645 -0.12020 -1.5719 -2.7249 0.062214 6.9747 4.3012 -0.46768 2.9521 -1.1730 22.155 -1.2408 -1.5319 0.95893 -4.3910 0.37811 -0.42066 -7.0624 0.075555 0.058533 -0.91342 -3.2283 -21.634 -0.12616 10.824 Gradients: -0.65081 0.18642 -6.3063 -0.17001 -0.16946 -0.023855 -0.24580 -1.0165 -0.22723 -0.060176 -1.3494 -1.1704 -0.087662 -10.521 0.053229 -0.33974 -0.12094 -0.029252 -3.9343 0.65023 0.040802 6.2992 0.16995 0.16942 0.023856 0.24558 1.0159 0.22712 0.060177 1.3492 1.1691 0.087640 10.509 -0.053557 0.33944 0.12083 0.029226 3.9325 5.1343e-005 (norm 7.13e-001) Iteration 26: loglikelihood = -128.541578159 (steplength = 0.015625) Parameters: 0.11029 7.1342 0.036126 -0.26532 2.0966 0.78167 -0.29774 -0.27199 -0.23649 -2.4151 0.56646 -0.025610 -0.075507 -0.035682 -0.24646 -0.12012 -1.5721 -2.7247 0.062206 6.9719 4.3011 -0.46754 2.9518 -1.1723 22.153 -1.2408 -1.5315 0.95956 -4.3941 0.37805 -0.42034 -7.0618 0.075534 0.058560 -0.91363 -3.2277 -21.633 -0.12608 10.843 Gradients: -0.51207 0.16536 -4.4916 -0.16634 -0.16167 -0.023450 -0.10865 -0.94780 -0.21855 -0.059061 -1.1828 -1.0209 -0.079205 -6.9387 0.13268 -0.21355 -0.10050 -0.027533 -3.6464 0.51107 0.047295 4.4790 0.16626 0.16160 0.023450 0.10802 0.94697 0.21842 0.059060 1.1821 1.0191 0.079159 6.9166 -0.13325 0.21286 0.10033 0.027502 3.6438 5.1147e-005 (norm 6.49e-001) Iteration 27: loglikelihood = -128.541555412 (steplength = 0.0078125) Parameters: 0.11035 7.1341 0.036128 -0.26531 2.0966 0.78137 -0.29777 -0.27199 -0.23649 -2.4151 0.56645 -0.025601 -0.075558 -0.035682 -0.24645 -0.12011 -1.5722 -2.7247 0.062201 6.9726 4.3013 -0.46767 2.9519 -1.1722 22.156 -1.2405 -1.5320 0.96000 -4.3948 0.37802 -0.42043 -7.0617 0.075546 0.058565 -0.91413 -3.2240 -21.635 -0.12600 10.853 Gradients: -0.40391 0.14193 -3.2894 -0.13937 -0.14578 -0.021755 -0.10676 -0.77810 -0.18745 -0.058137 -1.0691 -0.67279 -0.070487 -5.4270 0.16988 -0.18815 -0.076996 -0.020089 -3.1887 0.40288 0.016670 3.2765 0.13928 0.14571 0.021754 0.10614 0.77721 0.18732 0.058136 1.0683 0.67084 0.070437 5.4045 -0.17046 0.18747 0.076819 0.020055 3.1859 5.0377e-005 (norm 5.84e-001) Iteration 28: loglikelihood = -128.541544792 (steplength = 0.00390625) Parameters: 0.11040 7.1341 0.036126 -0.26531 2.0965 0.78131 -0.29776 -0.27199 -0.23652 -2.4151 0.56646 -0.025611 -0.075561 -0.035681 -0.24646 -0.12010 -1.5723 -2.7247 0.062199 6.9719 4.3012 -0.46763 2.9519 -1.1720 22.155 -1.2405 -1.5319 0.96015 -4.3956 0.37801 -0.42035 -7.0616 0.075540 0.058572 -0.91418 -3.2238 -21.635 -0.12598 10.857 Gradients: -0.50057 0.16153 -4.3921 -0.16246 -0.15790 -0.022886 -0.10589 -0.92657 -0.21361 -0.057683 -1.1541 -0.99874 -0.077307 -6.7818 0.12929 -0.20860 -0.098257 -0.026925 -3.5631 0.49950 0.046409 4.3787 0.16236 0.15782 0.022884 0.10527 0.92559 0.21346 0.057681 1.1533 0.99663 0.077254 6.7588 -0.12989 0.20791 0.098069 0.026888 3.5601 4.9990e-005 (norm 6.41e-001) Iteration 29: loglikelihood = -128.541518218 (steplength = 0.25) Parameters: 0.11033 7.1341 0.036128 -0.26532 2.0966 0.78131 -0.29775 -0.27198 -0.23649 -2.4150 0.56646 -0.025618 -0.075507 -0.035682 -0.24646 -0.12011 -1.5722 -2.7247 0.062202 6.9718 4.3013 -0.46763 2.9519 -1.1720 22.156 -1.2405 -1.5319 0.96015 -4.3956 0.37801 -0.42034 -7.0617 0.075541 0.058566 -0.91419 -3.2238 -21.635 -0.12598 11.196 Gradients: -0.61455 0.17798 -5.9056 -0.16356 -0.16305 -0.023019 -0.22508 -0.97410 -0.21860 -0.058230 -1.2877 -1.1099 -0.083851 -9.8138 0.060240 -0.31667 -0.11460 -0.027968 -3.7737 0.61347 0.039292 5.8921 0.16346 0.16297 0.023018 0.22446 0.97313 0.21844 0.058228 1.2869 1.1078 0.083797 9.7906 -0.060842 0.31597 0.11441 0.027931 3.7707 5.1166e-005 (norm 6.94e-001) Iteration 30: loglikelihood = -128.541493733 (steplength = 1) Parameters: 0.11015 7.1342 0.036129 -0.26534 2.0967 0.78171 -0.29773 -0.27197 -0.23638 -2.4151 0.56646 -0.025625 -0.075391 -0.035684 -0.24647 -0.12018 -1.5719 -2.7248 0.062212 6.9719 4.3013 -0.46765 2.9519 -1.1720 22.156 -1.2405 -1.5319 0.96029 -4.3957 0.37801 -0.42036 -7.0617 0.075541 0.058563 -0.91429 -3.2230 -21.635 -0.12596 12.058 Gradients: -0.13899 0.023021 -0.91101 0.0028061 -0.050486 0.0028849 0.047111 -0.37563 -0.10307 -0.041108 0.34283 0.030185 0.013309 -1.6335 0.069494 -0.10544 -0.017289 -0.0013872 -1.6071 0.13818 0.017239 0.90082 -0.0028802 0.050425 -0.0028858 -0.047580 0.37490 0.10296 0.041107 -0.34343 -0.031773 -0.013349 1.6161 -0.069948 0.10492 0.017147 0.0013592 1.6048 4.9775e-005 (norm 3.29e-001) Iteration 31: loglikelihood = -128.541484283 (steplength = 1) Parameters: 0.11015 7.1342 0.036129 -0.26534 2.0967 0.78169 -0.29773 -0.27197 -0.23638 -2.4150 0.56646 -0.025626 -0.075391 -0.035684 -0.24647 -0.12018 -1.5719 -2.7248 0.062211 6.9718 4.3013 -0.46765 2.9519 -1.1720 22.156 -1.2405 -1.5319 0.96031 -4.3958 0.37801 -0.42035 -7.0617 0.075541 0.058563 -0.91430 -3.2230 -21.635 -0.12596 12.927 Gradients: 0.23145 -0.065189 2.8173 0.15984 0.084782 0.015704 0.18563 0.10926 -0.0044692 -0.013842 1.1791 0.64063 0.082069 5.7696 0.12375 0.13527 0.047585 0.011922 0.87356 -0.23145 -0.0066678 -2.8173 -0.15984 -0.084782 -0.015704 -0.18563 -0.10926 0.0044705 0.013843 -1.1790 -0.64063 -0.082068 -5.7697 -0.12376 -0.13527 -0.047585 -0.011922 -0.87356 2.1246e-005 (norm 4.74e-001) Iteration 32: loglikelihood = -128.541483126 (steplength = 0.5) Parameters: 0.11014 7.1342 0.036129 -0.26534 2.0967 0.78171 -0.29773 -0.27197 -0.23638 -2.4151 0.56646 -0.025624 -0.075389 -0.035684 -0.24647 -0.12018 -1.5719 -2.7248 0.062212 6.9720 4.3013 -0.46765 2.9519 -1.1721 22.156 -1.2405 -1.5319 0.96021 -4.3955 0.37801 -0.42036 -7.0617 0.075541 0.058561 -0.91423 -3.2234 -21.635 -0.12598 13.470 Gradients: -0.42166 0.075901 -3.8065 -0.11323 -0.15895 -0.0030781 -0.047305 -0.72860 -0.17487 -0.063981 -0.097728 -0.33694 -0.031927 -7.7765 -0.0052671 -0.32008 -0.062471 -0.0085006 -3.6105 0.42166 0.036679 3.8065 0.11323 0.15895 0.0030782 0.047303 0.72860 0.17487 0.063981 0.097732 0.33694 0.031927 7.7764 0.0052638 0.32008 0.062471 0.0085006 3.6105 8.2885e-006 (norm 5.42e-001) Iteration 33: loglikelihood = -128.541480599 (steplength = 0.00195313) Parameters: 0.11016 7.1342 0.036129 -0.26534 2.0967 0.78166 -0.29773 -0.27197 -0.23639 -2.4150 0.56646 -0.025627 -0.075395 -0.035684 -0.24647 -0.12017 -1.5719 -2.7247 0.062211 6.9719 4.3013 -0.46765 2.9519 -1.1720 22.156 -1.2405 -1.5319 0.96030 -4.3958 0.37801 -0.42035 -7.0617 0.075541 0.058563 -0.91430 -3.2230 -21.635 -0.12596 13.472 Gradients: 0.044610 -0.010230 1.0296 0.068748 0.012640 0.0062505 0.12309 -0.16226 -0.060248 -0.027534 0.61310 0.25814 0.040030 2.4513 0.12425 0.038337 0.011618 0.0028457 -0.41105 -0.044640 0.0074586 -1.0299 -0.068749 -0.012641 -0.0062505 -0.12312 0.16224 0.060246 0.027534 -0.61313 -0.25817 -0.040032 -2.4521 -0.12427 -0.038365 -0.011622 -0.0028460 0.41099 4.9637e-006 (norm 3.05e-001) Iteration 34: loglikelihood = -128.541480419 (steplength = 0.015625) Parameters: 0.11016 7.1342 0.036129 -0.26534 2.0967 0.78167 -0.29773 -0.27197 -0.23638 -2.4150 0.56646 -0.025626 -0.075395 -0.035684 -0.24647 -0.12017 -1.5719 -2.7248 0.062211 6.9719 4.3013 -0.46765 2.9519 -1.1720 22.156 -1.2405 -1.5319 0.96027 -4.3957 0.37801 -0.42035 -7.0617 0.075541 0.058562 -0.91427 -3.2231 -21.635 -0.12597 13.489 Gradients: -0.091598 0.010110 -0.76163 0.065615 0.0054915 0.0059289 -0.013273 -0.22664 -0.067912 -0.028360 0.45075 0.11282 0.031848 -1.0927 0.044367 -0.086561 -0.0083541 0.0012395 -0.68250 0.091566 0.00080138 0.76121 -0.065616 -0.0054933 -0.0059289 0.013241 0.22662 0.067911 0.028360 -0.45078 -0.11285 -0.031850 1.0918 -0.044387 0.086532 0.0083495 -0.0012398 0.68243 4.9543e-006 (norm 2.91e-001) --- FINAL VALUES: loglikelihood = -128.541480419 (steplength = 0.015625) Parameters: 0.11016 7.1342 0.036129 -0.26534 2.0967 0.78167 -0.29773 -0.27197 -0.23638 -2.4150 0.56646 -0.025626 -0.075395 -0.035684 -0.24647 -0.12017 -1.5719 -2.7248 0.062211 6.9719 4.3013 -0.46765 2.9519 -1.1720 22.156 -1.2405 -1.5319 0.96027 -4.3957 0.37801 -0.42035 -7.0617 0.075541 0.058562 -0.91427 -3.2231 -21.635 -0.12597 13.489 Gradients: 0.044272 -0.010123 1.0175 0.067561 0.012435 0.0061443 0.12141 -0.15925 -0.059183 -0.027057 0.60307 0.25417 0.039363 2.4200 0.12235 0.038063 0.011481 0.0028024 -0.40322 -0.044312 0.0073540 -1.0180 -0.067562 -0.012437 -0.0061443 -0.12145 0.15923 0.059181 0.027057 -0.60311 -0.25421 -0.039366 -2.4211 -0.12237 -0.038100 -0.011487 -0.0028028 0.40314 4.8610e-006 (norm 3.02e-001) Successive criterion values within tolerance (1e-006) Model 3: Bivariate probit, using observations 1-296 Standard errors clustered by 82 values of i_firm coefficient std. error z p-value ---------------------------------------------------------- external: const 0.110160 1.48425 0.07422 0.9408 POT1 7.13424 1.18635 6.014 1.81e-09 *** TAILLE 0.0361287 0.0680595 0.5308 0.5955 INV_ANT −0.265339 0.516228 −0.5140 0.6073 CF_ANT 2.09666 1.30696 1.604 0.1087 VOLAT 0.781673 5.24522 0.1490 0.8815 D_DIV −0.297728 0.254035 −1.172 0.2412 SCOREZ −0.271968 0.331663 −0.8200 0.4122 D_V −0.236385 1.11381 −0.2122 0.8319 DRD −2.41504 3.57942 −0.6747 0.4999 MTB 0.566456 0.112937 5.016 5.28e-07 *** RFR −0.0256260 0.196319 −0.1305 0.8961 TANGI −0.0753952 1.00524 −0.07500 0.9402 AGE_BASE −0.0356837 0.0216510 −1.648 0.0993 * REND −0.246472 0.222779 −1.106 0.2686 TAXR −0.120172 0.521025 −0.2306 0.8176 DFC_IND2 −1.57189 3.78622 −0.4152 0.6780 PROFI −2.72475 2.78955 −0.9768 0.3287 SIC2DIGIT 0.0622110 0.0667275 0.9323 0.3512 equity: const 6.97190 2.69931 2.583 0.0098 *** POT2 4.30132 0.962711 4.468 7.90e-06 *** TAILLE −0.467652 0.179287 −2.608 0.0091 *** INV_ANT 2.95192 1.09282 2.701 0.0069 *** CF_ANT −1.17203 2.01875 −0.5806 0.5615 VOLAT 22.1561 7.75291 2.858 0.0043 *** D_DIV −1.24048 0.431633 −2.874 0.0041 *** SCOREZ −1.53191 0.651642 −2.351 0.0187 ** D_V 0.960271 1.29438 0.7419 0.4582 DRD −4.39569 7.20260 −0.6103 0.5417 MTB 0.378008 0.147044 2.571 0.0101 ** RFR −0.420355 0.323356 −1.300 0.1936 TANGI −7.06169 2.42802 −2.908 0.0036 *** AGE_BASE 0.0755413 0.0532725 1.418 0.1562 REND 0.0585621 0.257865 0.2271 0.8203 TAXR −0.914275 1.10068 −0.8306 0.4062 DFC_IND2 −3.22315 6.17726 −0.5218 0.6018 PROFI −21.6354 6.27013 −3.451 0.0006 *** SIC2DIGIT −0.125967 0.0890904 −1.414 0.1574 Log-likelihood −128.5415 Akaike criterion 335.0830 Schwarz criterion 479.0070 Hannan-Quinn 392.7073 rho = 1 Test of independence - Null hypothesis: rho = 0 Test statistic: Chi-square(1) = 16.4347 with p-value = 5.03547e-005 # genr matrix predict_prob = $yhat ? genr series predict_external = $yhat[,1] Replaced series predict_external (ID 63) ? genr series predict_equity = $yhat[,2] Replaced series predict_equity (ID 64) ? genr series prob_external = cdf(N,predict_external) Replaced series prob_external (ID 65) ? genr series prob_equity = cdf(N, predict_equity) Replaced series prob_equity (ID 66) ? genr correct_ext1 = sum((prob_external>=0.5) && external=1)/sum(external=1) Replaced scalar correct_ext1 = 0.512195 ? genr correct_ext0 = sum((prob_external<0.5) && external=0)/sum(external=0) Replaced scalar correct_ext0 = 0.96729 ? genr correct_ext = sum((prob_external>=0.5)==external)/$nobs Replaced scalar correct_ext = 0.841216 ? smpl external=1 --restrict Full data set: 2590 observations Current sample: 82 observations ? genr correct_eqt1 = sum((prob_equity>=0.5) && equity=1)/sum(equity=1) Replaced scalar correct_eqt1 = 0.454545 ? genr correct_eqt0 = sum((prob_equity<0.5) && equity=0)/sum(equity=0) Replaced scalar correct_eqt0 = 0.985915 ? genr correct_eqt = sum((prob_equity>=0.5)==equity)/$nobs Replaced scalar correct_eqt = 0.914634 ? smpl full Full data range: 1 - 2590 (n = 2590) # Impression des résultats de l'estimation ? print correct_ext1 correct_ext0 correct_ext correct_eqt1 correct_eqt0 \ correct_eqt correct_ext1 = 0.51219512 correct_ext0 = 0.96728972 correct_ext = 0.84121622 correct_eqt1 = 0.45454545 correct_eqt0 = 0.98591549 correct_eqt = 0.91463415