---------------------------------------------------------------------------------- log: C:\AAA Miker Files\current class files\methods tabular arrays\class 1 > 5.log log type: text opened on: 20 Nov 2002, 13:55:25 . use "C:\AAA Miker Files\current class files\methods tabular arrays\HW3 dataset w > ith best fit vars.dta", clear . poisson count _x_* Iteration 0: log likelihood = -20874866 (not concave) Iteration 1: log likelihood = -19204876 (not concave) Iteration 2: log likelihood = -17668486 (not concave) Iteration 3: log likelihood = -17527138 (not concave) Iteration 4: log likelihood = -17499096 (not concave) Iteration 5: log likelihood = -17143366 (not concave) Iteration 6: log likelihood = -17108516 (not concave) Iteration 7: log likelihood = -17052869 (not concave) Iteration 8: log likelihood = -15677459 (not concave) Iteration 9: log likelihood = -15665546 (not concave) Iteration 10: log likelihood = -15360818 (not concave) Iteration 11: log likelihood = -14882738 (not concave) Iteration 12: log likelihood = -9294531.8 (not concave) Iteration 13: log likelihood = -9203753.1 (not concave) Iteration 14: log likelihood = -8645839.5 (not concave) Iteration 15: log likelihood = -8597582.1 (not concave) Iteration 16: log likelihood = -8445555.5 (not concave) Iteration 17: log likelihood = -8416161.8 (not concave) Iteration 18: log likelihood = -8332423 (not concave) Iteration 19: log likelihood = -8159132 (not concave) Iteration 20: log likelihood = -8007582.7 (not concave) Iteration 21: log likelihood = -7858645 (not concave) Iteration 22: log likelihood = -7719216.2 (not concave) Iteration 23: log likelihood = -7452751.2 Iteration 24: log likelihood = -7449418 (not concave) Iteration 25: log likelihood = -7141992.5 (not concave) Iteration 26: log likelihood = -7019413.1 (not concave) Iteration 27: log likelihood = -6868438.9 (not concave) Iteration 28: log likelihood = -6706672 (not concave) --Break-- r(1); . poisson count _x_*, difficult Iteration 0: log likelihood = -20874866 (not concave) Iteration 1: log likelihood = -14166164 (not concave) Iteration 2: log likelihood = -7721669 (not concave) Iteration 3: log likelihood = -5445624.6 (not concave) Iteration 4: log likelihood = -4485363.8 (not concave) Iteration 5: log likelihood = -4067186.8 (not concave) Iteration 6: log likelihood = -3104618.8 (not concave) Iteration 7: log likelihood = -2773701.9 (not concave) Iteration 8: log likelihood = -2654307.9 (not concave) Iteration 9: log likelihood = -2402671.7 (not concave) Iteration 10: log likelihood = -2060858.2 (not concave) Iteration 11: log likelihood = -1946721.8 (not concave) Iteration 12: log likelihood = -1874847.7 (not concave) Iteration 13: log likelihood = -1116474.8 (not concave) Iteration 14: log likelihood = -650857.7 (not concave) Iteration 15: log likelihood = -167700.68 (not concave) Iteration 16: log likelihood = -76411.139 (not concave) Iteration 17: log likelihood = -12863.557 (not concave) Iteration 18: log likelihood = -2918.5109 Iteration 19: log likelihood = -2720.372 (backed up) Iteration 20: log likelihood = -2365.3539 Iteration 21: log likelihood = -1027.0238 Iteration 22: log likelihood = -740.32101 Iteration 23: log likelihood = -719.80168 Iteration 24: log likelihood = -718.80827 Iteration 25: log likelihood = -718.80351 Iteration 26: log likelihood = -718.80351 Poisson regression Number of obs = 225 LR chi2(158) = 4503789.69 Prob > chi2 = 0.0000 Log likelihood = -718.80351 Pseudo R2 = 0.9997 ------------------------------------------------------------------------------ count | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _x_1 | 3.240852 .8651314 3.75 0.000 1.545225 4.936478 _x_2 | 3.631501 .8504729 4.27 0.000 1.964605 5.298397 _x_3 | .3933432 .6818325 0.58 0.564 -.943024 1.72971 _x_4 | 1.720519 .6797327 2.53 0.011 .3882673 3.05277 _x_5 | -.3911822 .8455674 -0.46 0.644 -2.048464 1.2661 _x_6 | .5572776 .9147753 0.61 0.542 -1.235649 2.350204 _x_7 | -.1945544 .4585651 -0.42 0.671 -1.093325 .7042167 _x_8 | .3610171 .5826873 0.62 0.536 -.781029 1.503063 _x_9 | 1.389145 .8538391 1.63 0.104 -.2843492 3.062639 _x_10 | .5680446 .5822889 0.98 0.329 -.5732207 1.70931 _x_11 | -.1006331 .6598853 -0.15 0.879 -1.393984 1.192718 _x_12 | 1.453864 1.00498 1.45 0.148 -.5158598 3.423589 _x_13 | .2632353 .8453851 0.31 0.756 -1.393689 1.92016 _x_14 | 2.8094 .6123068 4.59 0.000 1.6093 4.009499 _x_15 | -1.136909 .5030555 -2.26 0.024 -2.122879 -.1509381 _x_16 | -1.646784 .4986444 -3.30 0.001 -2.624109 -.6694587 _x_17 | -3.162397 .7857727 -4.02 0.000 -4.702483 -1.62231 _x_18 | -.066743 .5143725 -0.13 0.897 -1.074895 .9414086 _x_19 | -1.579302 .574037 -2.75 0.006 -2.704394 -.4542104 _x_20 | -.7283368 .7249091 -1.00 0.315 -2.149133 .6924589 _x_21 | .1640403 .7128306 0.23 0.818 -1.233082 1.561163 _x_22 | -.3893206 .5912391 -0.66 0.510 -1.548128 .7694867 _x_23 | -.6636475 .4648095 -1.43 0.153 -1.574657 .2473623 _x_24 | -.3203404 .4942498 -0.65 0.517 -1.289052 .6483713 _x_25 | .1099051 .5774976 0.19 0.849 -1.021969 1.24178 _x_26 | 2.930877 .8142592 3.60 0.000 1.334958 4.526795 _x_27 | 1.016372 .4044791 2.51 0.012 .2236073 1.809136 _x_28 | .2754821 .3827666 0.72 0.472 -.4747266 1.025691 _x_29 | .5579576 .427629 1.30 0.192 -.2801799 1.396095 _x_30 | .5383146 .5927472 0.91 0.364 -.6234486 1.700078 _x_31 | .5538315 .8468498 0.65 0.513 -1.105964 2.213627 _x_32 | .3194109 .3774568 0.85 0.397 -.4203909 1.059213 _x_33 | -.14026 .5614656 -0.25 0.803 -1.240712 .9601924 _x_34 | .1416171 .6019911 0.24 0.814 -1.038264 1.321498 _x_35 | -.1754525 .6003804 -0.29 0.770 -1.352177 1.001271 _x_36 | 1.376614 .6301646 2.18 0.029 .1415138 2.611714 _x_37 | -.3861 .4743961 -0.81 0.416 -1.315899 .5436992 _x_38 | .5537738 .3750923 1.48 0.140 -.1813937 1.288941 _x_39 | .1269701 .5610421 0.23 0.821 -.9726523 1.226592 _x_40 | -.1415814 .8824882 -0.16 0.873 -1.871227 1.588064 _x_41 | .0771879 .4004585 0.19 0.847 -.7076962 .8620721 _x_42 | .4485211 .3178278 1.41 0.158 -.1744098 1.071452 _x_43 | 2.957806 1.093133 2.71 0.007 .8153048 5.100307 _x_44 | 4.12593 1.089509 3.79 0.000 1.990532 6.261329 _x_45 | 2.223097 .8006364 2.78 0.005 .6538781 3.792315 _x_46 | 2.799832 .870428 3.22 0.001 1.093825 4.50584 _x_47 | -.113412 .762534 -0.15 0.882 -1.607951 1.381127 _x_48 | -1.341631 1.097825 -1.22 0.222 -3.493328 .810067 _x_49 | .136736 .9656486 0.14 0.887 -1.7559 2.029372 _x_50 | -.6319624 .8036117 -0.79 0.432 -2.207012 .9430875 _x_51 | -1.495073 1.087844 -1.37 0.169 -3.627209 .637063 _x_52 | -1.655465 1.085255 -1.53 0.127 -3.782526 .471597 _x_53 | -.732152 .9520545 -0.77 0.442 -2.598144 1.13384 _x_54 | .0738822 .3962195 0.19 0.852 -.7026936 .8504581 _x_55 | -1.132259 .978029 -1.16 0.247 -3.049161 .7846422 _x_56 | -1.569231 .9631234 -1.63 0.103 -3.456918 .3184559 _x_57 | -1.823517 .7658908 -2.38 0.017 -3.324635 -.3223985 _x_58 | -2.308326 .7624837 -3.03 0.002 -3.802767 -.8138856 _x_59 | -2.94233 .9217373 -3.19 0.001 -4.748902 -1.135758 _x_60 | -.3838094 .7821068 -0.49 0.624 -1.91671 1.149092 _x_61 | .1468532 .7722014 0.19 0.849 -1.366634 1.66034 _x_62 | .5424095 .7715009 0.70 0.482 -.9697044 2.054523 _x_63 | .7416221 .8708722 0.85 0.394 -.965256 2.4485 _x_64 | 1.592066 .7586678 2.10 0.036 .1051041 3.079027 _x_65 | .779068 .7541013 1.03 0.302 -.6989434 2.257079 _x_66 | 1.329573 .7533233 1.76 0.078 -.1469138 2.806059 _x_67 | .5829629 .502862 1.16 0.246 -.4026285 1.568554 _x_68 | .0688317 .4922514 0.14 0.889 -.8959633 1.033627 _x_69 | .2654317 .407554 0.65 0.515 -.5333594 1.064223 _x_70 | -1.391217 .8820171 -1.58 0.115 -3.119939 .3375044 _x_71 | .2337931 .4716244 0.50 0.620 -.6905736 1.15816 _x_72 | -.2462281 .5715615 -0.43 0.667 -1.366468 .8740119 _x_73 | -.3552344 .4794538 -0.74 0.459 -1.294947 .5844777 _x_74 | -.1488438 .4715882 -0.32 0.752 -1.07314 .7754521 _x_75 | -.259767 .573589 -0.45 0.651 -1.383981 .8644469 _x_76 | -1.846626 .8561221 -2.16 0.031 -3.524594 -.1686574 _x_77 | .1634436 .5623891 0.29 0.771 -.9388188 1.265706 _x_78 | 1.112153 .7773951 1.43 0.153 -.4115131 2.63582 _x_79 | .0916919 .4213683 0.22 0.828 -.7341748 .9175586 _x_80 | 1.88784 .778489 2.43 0.015 .3620298 3.413651 _x_81 | 2.931957 .9033129 3.25 0.001 1.161496 4.702418 _x_82 | 2.050644 .9223877 2.22 0.026 .2427976 3.858491 _x_83 | .0673165 .775452 0.09 0.931 -1.452541 1.587175 _x_84 | 3.878742 .7993333 4.85 0.000 2.312078 5.445407 _x_85 | .6153313 .7151234 0.86 0.390 -.7862848 2.016947 _x_86 | .2403888 .7759769 0.31 0.757 -1.280498 1.761276 _x_87 | 4.422627 .9017943 4.90 0.000 2.655142 6.190111 _x_88 | 2.628049 .8161552 3.22 0.001 1.028414 4.227683 _x_89 | -1.503005 .9023434 -1.67 0.096 -3.271566 .2655553 _x_90 | -1.418561 .9019297 -1.57 0.116 -3.186311 .3491884 _x_91 | -.9011773 .7571036 -1.19 0.234 -2.385073 .5827185 _x_92 | -.2652421 .8259956 -0.32 0.748 -1.884164 1.35368 _x_93 | .6124922 1.044287 0.59 0.558 -1.434272 2.659256 _x_94 | .1735765 .4933891 0.35 0.725 -.7934484 1.140601 _x_95 | 4.075047 .8684025 4.69 0.000 2.373009 5.777084 _x_96 | -.4341477 .5541869 -0.78 0.433 -1.520334 .6520387 _x_97 | .471427 .7158723 0.66 0.510 -.9316568 1.874511 _x_98 | .5873786 .7748276 0.76 0.448 -.9312555 2.106013 _x_99 | -2.810266 .4422386 -6.35 0.000 -3.677038 -1.943495 _x_100 | -1.147101 .5513908 -2.08 0.037 -2.227807 -.066395 _x_101 | -.4301325 .8097312 -0.53 0.595 -2.017176 1.156911 _x_102 | -.3816067 .8094357 -0.47 0.637 -1.968071 1.204858 _x_103 | -.55438 .3150839 -1.76 0.078 -1.171933 .0631731 _x_104 | -.0700637 .4268858 -0.16 0.870 -.9067445 .7666171 _x_105 | -1.403857 .6463992 -2.17 0.030 -2.670776 -.1369375 _x_106 | 4.485583 .9317052 4.81 0.000 2.659474 6.311692 _x_107 | -.5450319 1.041892 -0.52 0.601 -2.587102 1.497038 _x_108 | -.6783284 .9005472 -0.75 0.451 -2.443369 1.086712 _x_109 | .9508847 .7713009 1.23 0.218 -.5608373 2.462607 _x_110 | .2466067 .4964691 0.50 0.619 -.7264548 1.219668 _x_111 | -1.828301 .3812952 -4.79 0.000 -2.575626 -1.080976 _x_112 | -.3122964 .4268834 -0.73 0.464 -1.148972 .5243796 _x_113 | -.7574335 .3313159 -2.29 0.022 -1.406801 -.1080663 _x_114 | 2.404666 .6443114 3.73 0.000 1.141839 3.667493 _x_115 | 3.530798 .5369753 6.58 0.000 2.478346 4.58325 _x_116 | -2.762625 1.103938 -2.50 0.012 -4.926303 -.5989458 _x_117 | 1.244483 .3852647 3.23 0.001 .4893779 1.999588 _x_118 | -.6418111 .8640754 -0.74 0.458 -2.335368 1.051745 _x_119 | -.7197791 1.084684 -0.66 0.507 -2.845721 1.406163 _x_120 | -1.111597 .8627789 -1.29 0.198 -2.802613 .5794184 _x_121 | -.4785383 .3827162 -1.25 0.211 -1.228648 .2715717 _x_122 | -1.723818 .3799054 -4.54 0.000 -2.468419 -.9792174 _x_123 | .2442174 .5821017 0.42 0.675 -.896681 1.385116 _x_124 | .0677447 .583763 0.12 0.908 -1.07641 1.211899 _x_125 | -.1726779 .312549 -0.55 0.581 -.7852626 .4399069 _x_126 | .4558527 .7306084 0.62 0.533 -.9761135 1.887819 _x_127 | -.593976 .7965269 -0.75 0.456 -2.15514 .9671881 _x_128 | -3.573128 .803433 -4.45 0.000 -5.147828 -1.998428 _x_129 | 1.831835 .7469858 2.45 0.014 .3677701 3.295901 _x_130 | -1.822488 .3981633 -4.58 0.000 -2.602874 -1.042102 _x_131 | -2.043109 .7594001 -2.69 0.007 -3.531506 -.554712 _x_132 | -.9960651 .7185663 -1.39 0.166 -2.404429 .4122989 _x_133 | -1.012057 .682507 -1.48 0.138 -2.349746 .3256319 _x_134 | -.7147765 .6826243 -1.05 0.295 -2.052696 .6231426 _x_135 | .6623433 .7062306 0.94 0.348 -.7218431 2.04653 _x_136 | .5181067 .7076267 0.73 0.464 -.8688161 1.905029 _x_137 | 1.457936 .7068992 2.06 0.039 .072439 2.843433 _x_138 | 1.695344 .7067501 2.40 0.016 .3101395 3.080549 _x_139 | -.7684451 .6633866 -1.16 0.247 -2.068659 .5317687 _x_140 | -.6365124 .6646068 -0.96 0.338 -1.939118 .6660931 _x_141 | .3530931 .3360759 1.05 0.293 -.3056035 1.01179 _x_142 | .58779 .3360607 1.75 0.080 -.070877 1.246457 _x_143 | -.2493807 .2791702 -0.89 0.372 -.7965442 .2977828 _x_144 | -.6760089 .2367569 -2.86 0.004 -1.140044 -.2119739 _x_145 | .0837018 .2307642 0.36 0.717 -.3685878 .5359913 _x_146 | .4385113 .2141736 2.05 0.041 .0187388 .8582838 _x_147 | 1.031243 .1684905 6.12 0.000 .7010076 1.361478 _x_148 | -.136437 .1747146 -0.78 0.435 -.4788713 .2059974 _x_149 | -.1947653 .3425154 -0.57 0.570 -.8660832 .4765525 _x_150 | .6912058 .3466955 1.99 0.046 .0116952 1.370716 _x_151 | -.3852562 .3218808 -1.20 0.231 -1.016131 .2456186 _x_152 | .4394303 .1746039 2.52 0.012 .097213 .7816476 _x_153 | .3494945 .7118407 0.49 0.623 -1.045688 1.744677 _x_154 | .606063 .7128631 0.85 0.395 -.7911231 2.003249 _x_155 | .895312 .325566 2.75 0.006 .2572143 1.53341 _x_156 | 1.193773 .6825468 1.75 0.080 -.1439946 2.53154 _x_157 | 1.217784 .682138 1.79 0.074 -.1191818 2.55475 _x_158 | -.0500916 .2918018 -0.17 0.864 -.6220127 .5218295 _cons | -3.25645 1.039817 -3.13 0.002 -5.294453 -1.218447 ------------------------------------------------------------------------------ . desrep ------------------------------------------------------------------------------- poisson ------------------------------------------------------------------------------- Dependent variable count Number of observations: 225 Initial log likelihood: -2252613.647 Log likelihood: -718.804 LR chi square: 4503789.688 Model degrees of freedom: 158 Pseudo R-squared: 1.000 Prob: 0.000 ------------------------------------------------------------------------------- nr Effect Coeff s.e. ------------------------------------------------------------------------------- count year 1 80 3.241** 0.865 2 90 3.632** 0.850 meth 3 Mex_Am 0.393 0.682 4 Oth_H 1.721* 0.680 5 Oth_NH -0.391 0.846 6 Wht_NH 0.557 0.915 year.meth 7 80.Mex_Am -0.195 0.459 8 80.Oth_H 0.361 0.583 9 80.Oth_NH 1.389 0.854 10 90.Mex_Am 0.568 0.582 11 90.Oth_H -0.101 0.660 12 90.Oth_NH 1.454 1.005 13 90.Wht_NH 0.263 0.845 mgen 14 US native 2.809** 0.612 meth.mgen 15 Mex_Am.US native -1.137* 0.503 16 Oth_H.US native -1.647** 0.499 17 Oth_NH.US native -3.162** 0.786 18 Wht_NH.US native -0.067 0.514 year.meth.mgen 19 80.Oth_H.US native -1.579** 0.574 20 80.Oth_NH.US native -0.728 0.725 21 80.Wht_NH.US native 0.164 0.713 22 90.Mex_Am.US native -0.389 0.591 23 90.Oth_H.US native -0.664 0.465 24 90.Oth_NH.US native -0.320 0.494 25 90.Wht_NH.US native 0.110 0.577 fgen 26 US native 2.931** 0.814 meth.fgen 27 Mex_Am.US native 1.016* 0.404 28 Oth_H.US native 0.275 0.383 29 Wht_NH.US native 0.558 0.428 year.meth.fgen 30 80.Mex_Am.US native 0.538 0.593 31 80.Wht_NH.US native 0.554 0.847 32 90.Oth_H.US native 0.319 0.377 33 90.Oth_NH.US native -0.140 0.561 year.mgen.fgen 34 80.US native.US native 0.142 0.602 35 90.US native.US native -0.175 0.600 meth.mgen.fgen 36 Oth_NH.US native.US native 1.377* 0.630 year.meth.mgen.fgen 37 80.Mex_Am.US native.US native -0.386 0.474 38 80.Oth_H.US native.US native 0.554 0.375 39 80.Oth_NH.US native.US native 0.127 0.561 40 80.Wht_NH.US native.US native -0.142 0.882 41 90.Mex_Am.US native.US native 0.077 0.400 42 90.Wht_NH.US native.US native 0.449 0.318 feth 43 Mex_Am 2.958** 1.093 44 Oth_H 4.126** 1.090 45 Oth_NH 2.223** 0.801 46 Wht_NH 2.800** 0.870 year.feth 47 80.Mex_Am -0.113 0.763 48 80.Oth_H -1.342 1.098 49 80.Oth_NH 0.137 0.966 50 80.Wht_NH -0.632 0.804 51 90.Mex_Am -1.495 1.088 52 90.Oth_H -1.655 1.085 53 90.Oth_NH -0.732 0.952 54 90.Wht_NH 0.074 0.396 year.fgen 55 80.US native -1.132 0.978 56 90.US native -1.569 0.963 feth.fgen 57 Mex_Am.US native -1.824* 0.766 58 Oth_H.US native -2.308** 0.762 59 Oth_NH.US native -2.942** 0.922 60 Wht_NH.US native -0.384 0.782 year.feth.fgen 61 80.Oth_H.US native 0.147 0.772 62 80.Oth_NH.US native 0.542 0.772 63 80.Wht_NH.US native 0.742 0.871 64 90.Mex_Am.US native 1.592* 0.759 65 90.Oth_H.US native 0.779 0.754 66 90.Oth_NH.US native 1.330 0.753 feth.mgen 67 Mex_Am.US native 0.583 0.503 68 Oth_H.US native 0.069 0.492 69 Wht_NH.US native 0.265 0.408 year.feth.mgen 70 80.Mex_Am.US native -1.391 0.882 71 80.Oth_H.US native 0.234 0.472 72 80.Oth_NH.US native -0.246 0.572 73 90.Mex_Am.US native -0.355 0.479 74 90.Oth_H.US native -0.149 0.472 75 90.Oth_NH.US native -0.260 0.574 76 90.Wht_NH.US native -1.847* 0.856 feth.fgen.mgen 77 Oth_NH.US native.US native 0.163 0.562 year.feth.fgen.mgen 78 80.Mex_Am.US native.US native 1.112 0.777 79 80.Wht_NH.US native.US native 0.092 0.421 80 90.Wht_NH.US native.US native 1.888* 0.778 ethintct 81 1 2.932** 0.903 82 2 2.051* 0.922 83 3 0.067 0.775 84 5 3.879** 0.799 ethintct.year 85 1.80 0.615 0.715 86 2.90 0.240 0.776 87 3.80 4.423** 0.902 88 3.90 2.628** 0.816 89 4.80 -1.503 0.902 90 4.90 -1.419 0.902 91 5.80 -0.901 0.757 92 5.90 -0.265 0.826 ethintct.fgen 93 1.US native 0.612 1.044 94 2.US native 0.174 0.493 95 4.US native 4.075** 0.868 96 5.US native -0.434 0.554 ethintct.year.fgen 97 1.90.US native 0.471 0.716 98 2.80.US native 0.587 0.775 99 3.80.US native -2.810** 0.442 100 3.90.US native -1.147* 0.551 101 4.80.US native -0.430 0.810 102 4.90.US native -0.382 0.809 103 5.90.US native -0.554 0.315 ethintct.mgen 104 2.US native -0.070 0.427 105 3.US native -1.404* 0.646 106 4.US native 4.486** 0.932 ethintct.year.mgen 107 1.80.US native -0.545 1.042 108 1.90.US native -0.678 0.901 109 2.80.US native 0.951 0.771 110 2.90.US native 0.247 0.496 111 3.80.US native -1.828** 0.381 112 5.80.US native -0.312 0.427 113 5.90.US native -0.757* 0.331 ethintct.fgen.mgen 114 1.US native.US native 2.405** 0.644 115 3.US native.US native 3.531** 0.537 116 4.US native.US native -2.763* 1.104 117 5.US native.US native 1.244** 0.385 ethintct.year.fgen.mgen 118 1.80.US native.US native -0.642 0.864 119 1.90.US native.US native -0.720 1.085 120 2.80.US native.US native -1.112 0.863 121 2.90.US native.US native -0.479 0.383 122 3.90.US native.US native -1.724** 0.380 123 4.80.US native.US native 0.244 0.582 124 4.90.US native.US native 0.068 0.584 125 5.80.US native.US native -0.173 0.313 QS 126 BW 0.456 0.731 127 Moh -0.594 0.797 128 BM -3.573** 0.803 129 WO 1.832* 0.747 130 OOh -1.822** 0.398 BOhS 131 1 -2.043** 0.759 BWS 132 1 -0.996 0.719 QS.year 133 BW.80 -1.012 0.683 134 BW.90 -0.715 0.683 135 Moh.80 0.662 0.706 136 Moh.90 0.518 0.708 137 BM.80 1.458* 0.707 138 BM.90 1.695* 0.707 139 WO.80 -0.768 0.663 140 WO.90 -0.637 0.665 141 OOh.80 0.353 0.336 142 OOh.90 0.588 0.336 QS.mgen 143 BW.US native -0.249 0.279 144 Moh.US native -0.676** 0.237 145 BM.US native 0.084 0.231 146 WO.US native 0.439* 0.214 147 OOh.US native 1.031** 0.168 QS.fgen 148 BW.US native -0.136 0.175 149 Moh.US native -0.195 0.343 150 BM.US native 0.691* 0.347 151 WO.US native -0.385 0.322 152 OOh.US native 0.439* 0.175 BOhS.year 153 1.80 0.349 0.712 154 1.90 0.606 0.713 BOhS.fgen 155 1.US native 0.895** 0.326 BWS.year 156 1.80 1.194 0.683 157 1.90 1.218 0.682 BWS.mgen 158 1.US native -0.050 0.292 159 _cons -3.256** 1.040 ------------------------------------------------------------------------------- * p < .05 ** p < .01 . poisgof Goodness-of-fit chi2 = 105.607 Prob > chi2(66) = 0.0014 . *This is one of the very good fitting models for the data from HW 3. . * BW=0.456 . *MoH=-0.594 . *And neither one is statistically significant. . poisson count _x_*, robust difficult Iteration 0: log likelihood = -20874866 (not concave) Iteration 1: log likelihood = -14166164 (not concave) Iteration 2: log likelihood = -7721669 (not concave) Iteration 3: log likelihood = -5445624.6 (not concave) Iteration 4: log likelihood = -4485363.8 (not concave) Iteration 5: log likelihood = -4067186.8 (not concave) Iteration 6: log likelihood = -3104618.8 (not concave) Iteration 7: log likelihood = -2773701.9 (not concave) Iteration 8: log likelihood = -2654307.9 (not concave) Iteration 9: log likelihood = -2402671.7 (not concave) Iteration 10: log likelihood = -2060858.2 (not concave) Iteration 11: log likelihood = -1946721.8 (not concave) Iteration 12: log likelihood = -1874847.7 (not concave) Iteration 13: log likelihood = -1116474.8 (not concave) Iteration 14: log likelihood = -650857.7 (not concave) Iteration 15: log likelihood = -167700.68 (not concave) Iteration 16: log likelihood = -76411.139 (not concave) Iteration 17: log likelihood = -12863.557 (not concave) Iteration 18: log likelihood = -2918.5109 Iteration 19: log likelihood = -2720.372 (backed up) Iteration 20: log likelihood = -2365.3539 Iteration 21: log likelihood = -1027.0238 Iteration 22: log likelihood = -740.32101 Iteration 23: log likelihood = -719.80168 Iteration 24: log likelihood = -718.80827 Iteration 25: log likelihood = -718.80351 Iteration 26: log likelihood = -718.80351 Poisson regression Number of obs = 225 Wald chi2(113) = . Prob > chi2 = . Log likelihood = -718.80351 Pseudo R2 = 0.9997 ------------------------------------------------------------------------------ | Robust count | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _x_1 | 3.240852 .8884981 3.65 0.000 1.499427 4.982276 _x_2 | 3.631501 .8786515 4.13 0.000 1.909376 5.353626 _x_3 | .3933432 .5060145 0.78 0.437 -.598427 1.385113 _x_4 | 1.720519 .4903546 3.51 0.000 .7594414 2.681596 _x_5 | -.3911822 .7429605 -0.53 0.599 -1.847358 1.064994 _x_6 | .5572776 .7295319 0.76 0.445 -.8725787 1.987134 _x_7 | -.1945544 .3162936 -0.62 0.538 -.8144784 .4253697 _x_8 | .3610171 .4324345 0.83 0.404 -.4865388 1.208573 _x_9 | 1.389145 .7375767 1.88 0.060 -.056479 2.834768 _x_10 | .5680446 .4156863 1.37 0.172 -.2466856 1.382775 _x_11 | -.1006331 .4288521 -0.23 0.814 -.9411678 .7399016 _x_12 | 1.453864 .9241612 1.57 0.116 -.3574582 3.265187 _x_13 | .2632353 .700693 0.38 0.707 -1.110098 1.636568 _x_14 | 2.8094 .4303943 6.53 0.000 1.965842 3.652957 _x_15 | -1.136909 .4024752 -2.82 0.005 -1.925746 -.3480718 _x_16 | -1.646784 .4040256 -4.08 0.000 -2.438659 -.8549081 _x_17 | -3.162397 .6496955 -4.87 0.000 -4.435776 -1.889017 _x_18 | -.066743 .4405677 -0.15 0.880 -.9302398 .7967538 _x_19 | -1.579302 .3602904 -4.38 0.000 -2.285458 -.873146 _x_20 | -.7283368 .6067298 -1.20 0.230 -1.917505 .4608317 _x_21 | .1640403 .5686955 0.29 0.773 -.9505825 1.278663 _x_22 | -.3893206 .4313133 -0.90 0.367 -1.234679 .4560379 _x_23 | -.6636475 .335296 -1.98 0.048 -1.320816 -.0064793 _x_24 | -.3203404 .3759925 -0.85 0.394 -1.057272 .4165914 _x_25 | .1099051 .4274711 0.26 0.797 -.727923 .9477331 _x_26 | 2.930877 .830572 3.53 0.000 1.302985 4.558768 _x_27 | 1.016372 .2780098 3.66 0.000 .4714826 1.561261 _x_28 | .2754821 .1589062 1.73 0.083 -.0359684 .5869326 _x_29 | .5579576 .1510095 3.69 0.000 .2619843 .8539309 _x_30 | .5383146 .4370587 1.23 0.218 -.3183046 1.394934 _x_31 | .5538315 .7172233 0.77 0.440 -.8519004 1.959563 _x_32 | .3194109 .1238301 2.58 0.010 .0767083 .5621135 _x_33 | -.14026 .4576483 -0.31 0.759 -1.037234 .7567142 _x_34 | .1416171 .3830797 0.37 0.712 -.6092054 .8924396 _x_35 | -.1754525 .3806085 -0.46 0.645 -.9214316 .5705265 _x_36 | 1.376614 .4820982 2.86 0.004 .4317186 2.321509 _x_37 | -.3861 .3466687 -1.11 0.265 -1.065558 .2933581 _x_38 | .5537738 .0982064 5.64 0.000 .3612928 .7462547 _x_39 | .1269701 .4640451 0.27 0.784 -.7825416 1.036482 _x_40 | -.1415814 .7117368 -0.20 0.842 -1.53656 1.253397 _x_41 | .0771879 .2735187 0.28 0.778 -.4588988 .6132747 _x_42 | .4485211 .1064371 4.21 0.000 .2399082 .6571341 _x_43 | 2.957806 1.308001 2.26 0.024 .394172 5.52144 _x_44 | 4.12593 1.241214 3.32 0.001 1.693196 6.558665 _x_45 | 2.223097 .8321762 2.67 0.008 .5920612 3.854132 _x_46 | 2.799832 .8198 3.42 0.001 1.193054 4.406611 _x_47 | -.113412 .7596552 -0.15 0.881 -1.602309 1.375485 _x_48 | -1.341631 1.224874 -1.10 0.273 -3.742339 1.059078 _x_49 | .136736 .8896212 0.15 0.878 -1.60689 1.880361 _x_50 | -.6319624 .8462652 -0.75 0.455 -2.290612 1.026687 _x_51 | -1.495073 1.279023 -1.17 0.242 -4.001913 1.011767 _x_52 | -1.655465 1.212031 -1.37 0.172 -4.031002 .7200729 _x_53 | -.732152 .8824222 -0.83 0.407 -2.461668 .9973637 _x_54 | .0738822 .1811138 0.41 0.683 -.2810943 .4288588 _x_55 | -1.132259 .935811 -1.21 0.226 -2.966415 .7018965 _x_56 | -1.569231 .9276567 -1.69 0.091 -3.387405 .2489426 _x_57 | -1.823517 .871523 -2.09 0.036 -3.531671 -.1153631 _x_58 | -2.308326 .874118 -2.64 0.008 -4.021566 -.5950865 _x_59 | -2.94233 .8551132 -3.44 0.001 -4.618321 -1.266339 _x_60 | -.3838094 .8284513 -0.46 0.643 -2.007544 1.239925 _x_61 | .1468532 .8527383 0.17 0.863 -1.524483 1.81819 _x_62 | .5424095 .8512108 0.64 0.524 -1.125933 2.210752 _x_63 | .7416221 .8877771 0.84 0.404 -.998389 2.481633 _x_64 | 1.592066 .845239 1.88 0.060 -.0645725 3.248704 _x_65 | .779068 .8465486 0.92 0.357 -.8801367 2.438273 _x_66 | 1.329573 .8448083 1.57 0.116 -.3262211 2.985367 _x_67 | .5829629 .4544251 1.28 0.200 -.3076939 1.47362 _x_68 | .0688317 .2791412 0.25 0.805 -.4782751 .6159385 _x_69 | .2654317 .1732888 1.53 0.126 -.0742081 .6050715 _x_70 | -1.391217 .9418014 -1.48 0.140 -3.237114 .4546797 _x_71 | .2337931 .2337582 1.00 0.317 -.2243645 .6919508 _x_72 | -.2462281 .2624685 -0.94 0.348 -.760657 .2682008 _x_73 | -.3552344 .4147618 -0.86 0.392 -1.168153 .4576838 _x_74 | -.1488438 .2257488 -0.66 0.510 -.5913032 .2936157 _x_75 | -.259767 .2635876 -0.99 0.324 -.7763891 .2568552 _x_76 | -1.846626 .8840928 -2.09 0.037 -3.579416 -.1138358 _x_77 | .1634436 .2573096 0.64 0.525 -.3408739 .6677611 _x_78 | 1.112153 .8511606 1.31 0.191 -.556091 2.780397 _x_79 | .0916919 .1910587 0.48 0.631 -.2827762 .4661601 _x_80 | 1.88784 .8642174 2.18 0.029 .1940053 3.581675 _x_81 | 2.931957 .8575471 3.42 0.001 1.251196 4.612718 _x_82 | 2.050644 .8469756 2.42 0.015 .3906025 3.710686 _x_83 | .0673165 .6673819 0.10 0.920 -1.240728 1.375361 _x_84 | 3.878742 .6421816 6.04 0.000 2.620089 5.137395 _x_85 | .6153313 .4550087 1.35 0.176 -.2764693 1.507132 _x_86 | .2403888 .780842 0.31 0.758 -1.290033 1.770811 _x_87 | 4.422627 .6820556 6.48 0.000 3.085822 5.759431 _x_88 | 2.628049 .6309022 4.17 0.000 1.391503 3.864594 _x_89 | -1.503005 .8596189 -1.75 0.080 -3.187827 .1818167 _x_90 | -1.418561 .8478193 -1.67 0.094 -3.080257 .243134 _x_91 | -.9011773 .6977474 -1.29 0.197 -2.268737 .4663826 _x_92 | -.2652421 .6821283 -0.39 0.697 -1.602189 1.071705 _x_93 | .6124922 .9328912 0.66 0.511 -1.215941 2.440925 _x_94 | .1735765 .3034808 0.57 0.567 -.4212348 .7683879 _x_95 | 4.075047 .6939947 5.87 0.000 2.714842 5.435251 _x_96 | -.4341477 .3799067 -1.14 0.253 -1.178751 .3104557 _x_97 | .471427 .444267 1.06 0.289 -.3993203 1.342174 _x_98 | .5873786 .7911568 0.74 0.458 -.9632602 2.138017 _x_99 | -2.810266 .1349743 -20.82 0.000 -3.074811 -2.545722 _x_100 | -1.147101 .2477216 -4.63 0.000 -1.632627 -.6615756 _x_101 | -.4301325 .573272 -0.75 0.453 -1.553725 .69346 _x_102 | -.3816067 .5651922 -0.68 0.500 -1.489363 .7261498 _x_103 | -.55438 .1825699 -3.04 0.002 -.9122103 -.1965496 _x_104 | -.0700637 .4810992 -0.15 0.884 -1.013001 .8728733 _x_105 | -1.403857 .3399079 -4.13 0.000 -2.070064 -.7376493 _x_106 | 4.485583 .8705273 5.15 0.000 2.779381 6.191785 _x_107 | -.5450319 .935811 -0.58 0.560 -2.379188 1.289124 _x_108 | -.6783284 .8786515 -0.77 0.440 -2.400454 1.043797 _x_109 | .9508847 .6445651 1.48 0.140 -.3124397 2.214209 _x_110 | .2466067 .503501 0.49 0.624 -.740237 1.23345 _x_111 | -1.828301 .2344316 -7.80 0.000 -2.287778 -1.368823 _x_112 | -.3122964 .3076899 -1.01 0.310 -.9153575 .2907647 _x_113 | -.7574335 .2621631 -2.89 0.004 -1.271264 -.2436033 _x_114 | 2.404666 .4303943 5.59 0.000 1.561109 3.248223 _x_115 | 3.530798 .2239114 15.77 0.000 3.09194 3.969656 _x_116 | -2.762625 .952315 -2.90 0.004 -4.629128 -.8961215 _x_117 | 1.244483 .3273453 3.80 0.000 .6028979 1.886068 _x_118 | -.6418111 .8532801 -0.75 0.452 -2.314209 1.030587 _x_119 | -.7197791 .9464158 -0.76 0.447 -2.57472 1.135162 _x_120 | -1.111597 .8092821 -1.37 0.170 -2.697761 .4745666 _x_121 | -.4785383 .2732082 -1.75 0.080 -1.014017 .05694 _x_122 | -1.723818 .2141691 -8.05 0.000 -2.143582 -1.304055 _x_123 | .2442174 .3376124 0.72 0.469 -.4174907 .9059254 _x_124 | .0677447 .3317784 0.20 0.838 -.5825291 .7180184 _x_125 | -.1726779 .1694822 -1.02 0.308 -.5048569 .1595012 _x_126 | .4558527 .6894574 0.66 0.508 -.8954589 1.807164 _x_127 | -.593976 .7030128 -0.84 0.398 -1.971856 .7839038 _x_128 | -3.573128 .6378845 -5.60 0.000 -4.823359 -2.322897 _x_129 | 1.831835 .6194486 2.96 0.003 .6177385 3.045932 _x_130 | -1.822488 .3274085 -5.57 0.000 -2.464197 -1.180779 _x_131 | -2.043109 .598021 -3.42 0.001 -3.215208 -.8710094 _x_132 | -.9960651 .6153373 -1.62 0.106 -2.202104 .2099738 _x_133 | -1.012057 .6337938 -1.60 0.110 -2.25427 .2301558 _x_134 | -.7147765 .6181044 -1.16 0.248 -1.926239 .4966858 _x_135 | .6623433 .6427343 1.03 0.303 -.5973927 1.922079 _x_136 | .5181067 .6315897 0.82 0.412 -.7197863 1.756 _x_137 | 1.457936 .5873909 2.48 0.013 .306671 2.609201 _x_138 | 1.695344 .5749411 2.95 0.003 .5684803 2.822208 _x_139 | -.7684451 .5724228 -1.34 0.179 -1.890373 .353483 _x_140 | -.6365124 .5636638 -1.13 0.259 -1.741273 .4682484 _x_141 | .3530931 .2658517 1.33 0.184 -.1679666 .8741528 _x_142 | .58779 .2593568 2.27 0.023 .07946 1.09612 _x_143 | -.2493807 .2981292 -0.84 0.403 -.8337033 .3349419 _x_144 | -.6760089 .2187246 -3.09 0.002 -1.104701 -.2473167 _x_145 | .0837018 .1758878 0.48 0.634 -.261032 .4284356 _x_146 | .4385113 .1799473 2.44 0.015 .0858212 .7912015 _x_147 | 1.031243 .1485008 6.94 0.000 .7401867 1.322299 _x_148 | -.136437 .1497992 -0.91 0.362 -.430038 .1571641 _x_149 | -.1947653 .1793406 -1.09 0.277 -.5462665 .1567358 _x_150 | .6912058 .2024748 3.41 0.001 .2943624 1.088049 _x_151 | -.3852562 .1354994 -2.84 0.004 -.6508302 -.1196823 _x_152 | .4394303 .1539103 2.86 0.004 .1377716 .7410889 _x_153 | .3494945 .5800726 0.60 0.547 -.7874269 1.486416 _x_154 | .606063 .5699171 1.06 0.288 -.5109539 1.72308 _x_155 | .895312 .1246789 7.18 0.000 .6509459 1.139678 _x_156 | 1.193773 .5809485 2.05 0.040 .0551344 2.332411 _x_157 | 1.217784 .5656263 2.15 0.031 .1091768 2.326391 _x_158 | -.0500916 .2834157 -0.18 0.860 -.6055761 .5053929 _cons | -3.25645 .9530785 -3.42 0.001 -5.12445 -1.388451 ------------------------------------------------------------------------------ . desrep ------------------------------------------------------------------------------- poisson ------------------------------------------------------------------------------- Dependent variable count Number of observations: 225 Type of standard error Robust Initial log likelihood: -2252613.647 Log likelihood: -718.804 Wald chi square: . Model degrees of freedom: 113 Pseudo R-squared: 1.000 Prob: . ------------------------------------------------------------------------------- nr Effect Coeff s.e. ------------------------------------------------------------------------------- count year 1 80 3.241** 0.888 2 90 3.632** 0.879 meth 3 Mex_Am 0.393 0.506 4 Oth_H 1.721** 0.490 5 Oth_NH -0.391 0.743 6 Wht_NH 0.557 0.730 year.meth 7 80.Mex_Am -0.195 0.316 8 80.Oth_H 0.361 0.432 9 80.Oth_NH 1.389 0.738 10 90.Mex_Am 0.568 0.416 11 90.Oth_H -0.101 0.429 12 90.Oth_NH 1.454 0.924 13 90.Wht_NH 0.263 0.701 mgen 14 US native 2.809** 0.430 meth.mgen 15 Mex_Am.US native -1.137** 0.402 16 Oth_H.US native -1.647** 0.404 17 Oth_NH.US native -3.162** 0.650 18 Wht_NH.US native -0.067 0.441 year.meth.mgen 19 80.Oth_H.US native -1.579** 0.360 20 80.Oth_NH.US native -0.728 0.607 21 80.Wht_NH.US native 0.164 0.569 22 90.Mex_Am.US native -0.389 0.431 23 90.Oth_H.US native -0.664* 0.335 24 90.Oth_NH.US native -0.320 0.376 25 90.Wht_NH.US native 0.110 0.427 fgen 26 US native 2.931** 0.831 meth.fgen 27 Mex_Am.US native 1.016** 0.278 28 Oth_H.US native 0.275 0.159 29 Wht_NH.US native 0.558** 0.151 year.meth.fgen 30 80.Mex_Am.US native 0.538 0.437 31 80.Wht_NH.US native 0.554 0.717 32 90.Oth_H.US native 0.319** 0.124 33 90.Oth_NH.US native -0.140 0.458 year.mgen.fgen 34 80.US native.US native 0.142 0.383 35 90.US native.US native -0.175 0.381 meth.mgen.fgen 36 Oth_NH.US native.US native 1.377** 0.482 year.meth.mgen.fgen 37 80.Mex_Am.US native.US native -0.386 0.347 38 80.Oth_H.US native.US native 0.554** 0.098 39 80.Oth_NH.US native.US native 0.127 0.464 40 80.Wht_NH.US native.US native -0.142 0.712 41 90.Mex_Am.US native.US native 0.077 0.274 42 90.Wht_NH.US native.US native 0.449** 0.106 feth 43 Mex_Am 2.958* 1.308 44 Oth_H 4.126** 1.241 45 Oth_NH 2.223** 0.832 46 Wht_NH 2.800** 0.820 year.feth 47 80.Mex_Am -0.113 0.760 48 80.Oth_H -1.342 1.225 49 80.Oth_NH 0.137 0.890 50 80.Wht_NH -0.632 0.846 51 90.Mex_Am -1.495 1.279 52 90.Oth_H -1.655 1.212 53 90.Oth_NH -0.732 0.882 54 90.Wht_NH 0.074 0.181 year.fgen 55 80.US native -1.132 0.936 56 90.US native -1.569 0.928 feth.fgen 57 Mex_Am.US native -1.824* 0.872 58 Oth_H.US native -2.308** 0.874 59 Oth_NH.US native -2.942** 0.855 60 Wht_NH.US native -0.384 0.828 year.feth.fgen 61 80.Oth_H.US native 0.147 0.853 62 80.Oth_NH.US native 0.542 0.851 63 80.Wht_NH.US native 0.742 0.888 64 90.Mex_Am.US native 1.592 0.845 65 90.Oth_H.US native 0.779 0.847 66 90.Oth_NH.US native 1.330 0.845 feth.mgen 67 Mex_Am.US native 0.583 0.454 68 Oth_H.US native 0.069 0.279 69 Wht_NH.US native 0.265 0.173 year.feth.mgen 70 80.Mex_Am.US native -1.391 0.942 71 80.Oth_H.US native 0.234 0.234 72 80.Oth_NH.US native -0.246 0.262 73 90.Mex_Am.US native -0.355 0.415 74 90.Oth_H.US native -0.149 0.226 75 90.Oth_NH.US native -0.260 0.264 76 90.Wht_NH.US native -1.847* 0.884 feth.fgen.mgen 77 Oth_NH.US native.US native 0.163 0.257 year.feth.fgen.mgen 78 80.Mex_Am.US native.US native 1.112 0.851 79 80.Wht_NH.US native.US native 0.092 0.191 80 90.Wht_NH.US native.US native 1.888* 0.864 ethintct 81 1 2.932** 0.858 82 2 2.051* 0.847 83 3 0.067 0.667 84 5 3.879** 0.642 ethintct.year 85 1.80 0.615 0.455 86 2.90 0.240 0.781 87 3.80 4.423** 0.682 88 3.90 2.628** 0.631 89 4.80 -1.503 0.860 90 4.90 -1.419 0.848 91 5.80 -0.901 0.698 92 5.90 -0.265 0.682 ethintct.fgen 93 1.US native 0.612 0.933 94 2.US native 0.174 0.303 95 4.US native 4.075** 0.694 96 5.US native -0.434 0.380 ethintct.year.fgen 97 1.90.US native 0.471 0.444 98 2.80.US native 0.587 0.791 99 3.80.US native -2.810** 0.135 100 3.90.US native -1.147** 0.248 101 4.80.US native -0.430 0.573 102 4.90.US native -0.382 0.565 103 5.90.US native -0.554** 0.183 ethintct.mgen 104 2.US native -0.070 0.481 105 3.US native -1.404** 0.340 106 4.US native 4.486** 0.871 ethintct.year.mgen 107 1.80.US native -0.545 0.936 108 1.90.US native -0.678 0.879 109 2.80.US native 0.951 0.645 110 2.90.US native 0.247 0.504 111 3.80.US native -1.828** 0.234 112 5.80.US native -0.312 0.308 113 5.90.US native -0.757** 0.262 ethintct.fgen.mgen 114 1.US native.US native 2.405** 0.430 115 3.US native.US native 3.531** 0.224 116 4.US native.US native -2.763** 0.952 117 5.US native.US native 1.244** 0.327 ethintct.year.fgen.mgen 118 1.80.US native.US native -0.642 0.853 119 1.90.US native.US native -0.720 0.946 120 2.80.US native.US native -1.112 0.809 121 2.90.US native.US native -0.479 0.273 122 3.90.US native.US native -1.724** 0.214 123 4.80.US native.US native 0.244 0.338 124 4.90.US native.US native 0.068 0.332 125 5.80.US native.US native -0.173 0.169 QS 126 BW 0.456 0.689 127 Moh -0.594 0.703 128 BM -3.573** 0.638 129 WO 1.832** 0.619 130 OOh -1.822** 0.327 BOhS 131 1 -2.043** 0.598 BWS 132 1 -0.996 0.615 QS.year 133 BW.80 -1.012 0.634 134 BW.90 -0.715 0.618 135 Moh.80 0.662 0.643 136 Moh.90 0.518 0.632 137 BM.80 1.458* 0.587 138 BM.90 1.695** 0.575 139 WO.80 -0.768 0.572 140 WO.90 -0.637 0.564 141 OOh.80 0.353 0.266 142 OOh.90 0.588* 0.259 QS.mgen 143 BW.US native -0.249 0.298 144 Moh.US native -0.676** 0.219 145 BM.US native 0.084 0.176 146 WO.US native 0.439* 0.180 147 OOh.US native 1.031** 0.149 QS.fgen 148 BW.US native -0.136 0.150 149 Moh.US native -0.195 0.179 150 BM.US native 0.691** 0.202 151 WO.US native -0.385** 0.135 152 OOh.US native 0.439** 0.154 BOhS.year 153 1.80 0.349 0.580 154 1.90 0.606 0.570 BOhS.fgen 155 1.US native 0.895** 0.125 BWS.year 156 1.80 1.194* 0.581 157 1.90 1.218* 0.566 BWS.mgen 158 1.US native -0.050 0.283 159 _cons -3.256** 0.953 ------------------------------------------------------------------------------- * p < .05 ** p < .01 . *The coefficients are exactly the same and the standard errors are very simi > lar, because this model fits well. . poisgof Goodness-of-fit chi2 = 105.607 Prob > chi2(111) = 0.6267 . poisson count _x_*, robust Iteration 0: log likelihood = -20874866 (not concave) Iteration 1: log likelihood = -19204876 (not concave) Iteration 2: log likelihood = -17668486 (not concave) Iteration 3: log likelihood = -17527138 (not concave) Iteration 4: log likelihood = -17499096 (not concave) Iteration 5: log likelihood = -17143366 (not concave) Iteration 6: log likelihood = -17108516 (not concave) Iteration 7: log likelihood = -17052869 (not concave) Iteration 8: log likelihood = -15677459 (not concave) Iteration 9: log likelihood = -15665546 (not concave) Iteration 10: log likelihood = -15360818 (not concave) Iteration 11: log likelihood = -14882738 (not concave) Iteration 12: log likelihood = -9294531.8 (not concave) Iteration 13: log likelihood = -9203753.1 (not concave) Iteration 14: log likelihood = -8645839.5 (not concave) Iteration 15: log likelihood = -8597582.1 (not concave) Iteration 16: log likelihood = -8445555.5 (not concave) Iteration 17: log likelihood = -8416161.8 (not concave) Iteration 18: log likelihood = -8332423 (not concave) Iteration 19: log likelihood = -8159132 (not concave) Iteration 20: log likelihood = -8007582.7 (not concave) Iteration 21: log likelihood = -7858645 (not concave) Iteration 22: log likelihood = -7719216.2 (not concave) Iteration 23: log likelihood = -7452751.2 (not concave) Iteration 24: log likelihood = -7182121.6 (not concave) Iteration 25: log likelihood = -7036814.9 (not concave) Iteration 26: log likelihood = -6620600.4 (not concave) Iteration 27: log likelihood = -6586938.5 (not concave) Iteration 28: log likelihood = -6310839.2 (not concave) Iteration 29: log likelihood = -6231458.2 (not concave) Iteration 30: log likelihood = -5988940.7 (not concave) Iteration 31: log likelihood = -5917634.3 (not concave) Iteration 32: log likelihood = -5648678.8 (not concave) Iteration 33: log likelihood = -5569122.5 (not concave) Iteration 34: log likelihood = -5355395.3 (not concave) Iteration 35: log likelihood = -5196922.2 (not concave) Iteration 36: log likelihood = -5033601 (not concave) Iteration 37: log likelihood = -4923389.3 (not concave) Iteration 38: log likelihood = -4807604.1 (not concave) Iteration 39: log likelihood = -4627704.3 (not concave) Iteration 40: log likelihood = -4447277.5 (not concave) Iteration 41: log likelihood = -3936970.2 (not concave) Iteration 42: log likelihood = -3526648.3 (not concave) Iteration 43: log likelihood = -3091205.5 (not concave) Iteration 44: log likelihood = -2986822.2 (not concave) Iteration 45: log likelihood = -2836663 (not concave) Iteration 46: log likelihood = -2737468.9 (not concave) Iteration 47: log likelihood = -2633142.1 (not concave) Iteration 48: log likelihood = -2461304.6 (not concave) Iteration 49: log likelihood = -2306160.4 (not concave) Iteration 50: log likelihood = -2080610.8 (not concave) Iteration 51: log likelihood = -1945209.4 (not concave) Iteration 52: log likelihood = -1836909 (not concave) Iteration 53: log likelihood = -1749926.2 (not concave) Iteration 54: log likelihood = -1665907.8 (not concave) Iteration 55: log likelihood = -1583248 (not concave) Iteration 56: log likelihood = -1498145.4 (not concave) Iteration 57: log likelihood = -1480559.3 (not concave) Iteration 58: log likelihood = -1465842.3 (not concave) Iteration 59: log likelihood = -1452129.1 (not concave) Iteration 60: log likelihood = -1438798.6 (not concave) Iteration 61: log likelihood = -1426074.4 (not concave) Iteration 62: log likelihood = -1414635.7 (not concave) Iteration 63: log likelihood = -1404038.5 (not concave) Iteration 64: log likelihood = -1394440.4 (not concave) Iteration 65: log likelihood = -1385201.9 (not concave) Iteration 66: log likelihood = -1376368.8 (not concave) Iteration 67: log likelihood = -1367598.3 (not concave) Iteration 68: log likelihood = -1359060.3 (not concave) Iteration 69: log likelihood = -1350560.1 (not concave) Iteration 70: log likelihood = -1342276.4 (not concave) Iteration 71: log likelihood = -1334028.3 (not concave) Iteration 72: log likelihood = -1326007.2 (not concave) Iteration 73: log likelihood = -1318061.2 (not concave) Iteration 74: log likelihood = -1310384 (not concave) Iteration 75: log likelihood = -1302836.6 (not concave) Iteration 76: log likelihood = -1295602.2 (not concave) Iteration 77: log likelihood = -1288534.3 (not concave) Iteration 78: log likelihood = -1281773.7 (not concave) Iteration 79: log likelihood = -1275151.2 (not concave) Iteration 80: log likelihood = -1268764.2 (not concave) Iteration 81: log likelihood = -1262447.7 (not concave) Iteration 82: log likelihood = -1256288.7 (not concave) Iteration 83: log likelihood = -1250149.1 (not concave) Iteration 84: log likelihood = -1244122.9 (not concave) Iteration 85: log likelihood = -1238090.9 (not concave) Iteration 86: log likelihood = -1232151.4 (not concave) Iteration 87: log likelihood = -1226194.8 (not concave) Iteration 88: log likelihood = -1220321 (not concave) Iteration 89: log likelihood = -1214425.3 (not concave) Iteration 90: log likelihood = -1208606.8 (not concave) Iteration 91: log likelihood = -1202762.9 (not concave) Iteration 92: log likelihood = -1196990.8 (not concave) Iteration 93: log likelihood = -1191188.4 (not concave) Iteration 94: log likelihood = -1185451.5 (not concave) Iteration 95: log likelihood = -1179679.9 (not concave) Iteration 96: log likelihood = -1173969.5 (not concave) Iteration 97: log likelihood = -1168221.9 (not concave) --Break-- r(1); . poisson count _x_*, robust difficult Iteration 0: log likelihood = -20874866 (not concave) Iteration 1: log likelihood = -14166164 (not concave) Iteration 2: log likelihood = -7721669 (not concave) Iteration 3: log likelihood = -5445624.6 (not concave) Iteration 4: log likelihood = -4485363.8 (not concave) Iteration 5: log likelihood = -4067186.8 (not concave) Iteration 6: log likelihood = -3104618.8 (not concave) Iteration 7: log likelihood = -2773701.9 (not concave) Iteration 8: log likelihood = -2654307.9 (not concave) Iteration 9: log likelihood = -2402671.7 (not concave) Iteration 10: log likelihood = -2060858.2 (not concave) Iteration 11: log likelihood = -1946721.8 (not concave) Iteration 12: log likelihood = -1874847.7 (not concave) Iteration 13: log likelihood = -1116474.8 (not concave) Iteration 14: log likelihood = -650857.7 (not concave) Iteration 15: log likelihood = -167700.68 (not concave) Iteration 16: log likelihood = -76411.139 (not concave) Iteration 17: log likelihood = -12863.557 (not concave) Iteration 18: log likelihood = -2918.5109 Iteration 19: log likelihood = -2720.372 (backed up) Iteration 20: log likelihood = -2365.3539 Iteration 21: log likelihood = -1027.0238 Iteration 22: log likelihood = -740.32101 Iteration 23: log likelihood = -719.80168 Iteration 24: log likelihood = -718.80827 Iteration 25: log likelihood = -718.80351 Iteration 26: log likelihood = -718.80351 Poisson regression Number of obs = 225 Wald chi2(113) = . Prob > chi2 = . Log likelihood = -718.80351 Pseudo R2 = 0.9997 ------------------------------------------------------------------------------ | Robust count | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _x_1 | 3.240852 .8884981 3.65 0.000 1.499427 4.982276 _x_2 | 3.631501 .8786515 4.13 0.000 1.909376 5.353626 _x_3 | .3933432 .5060145 0.78 0.437 -.598427 1.385113 _x_4 | 1.720519 .4903546 3.51 0.000 .7594414 2.681596 _x_5 | -.3911822 .7429605 -0.53 0.599 -1.847358 1.064994 _x_6 | .5572776 .7295319 0.76 0.445 -.8725787 1.987134 _x_7 | -.1945544 .3162936 -0.62 0.538 -.8144784 .4253697 _x_8 | .3610171 .4324345 0.83 0.404 -.4865388 1.208573 _x_9 | 1.389145 .7375767 1.88 0.060 -.056479 2.834768 _x_10 | .5680446 .4156863 1.37 0.172 -.2466856 1.382775 _x_11 | -.1006331 .4288521 -0.23 0.814 -.9411678 .7399016 _x_12 | 1.453864 .9241612 1.57 0.116 -.3574582 3.265187 _x_13 | .2632353 .700693 0.38 0.707 -1.110098 1.636568 _x_14 | 2.8094 .4303943 6.53 0.000 1.965842 3.652957 _x_15 | -1.136909 .4024752 -2.82 0.005 -1.925746 -.3480718 _x_16 | -1.646784 .4040256 -4.08 0.000 -2.438659 -.8549081 _x_17 | -3.162397 .6496955 -4.87 0.000 -4.435776 -1.889017 _x_18 | -.066743 .4405677 -0.15 0.880 -.9302398 .7967538 _x_19 | -1.579302 .3602904 -4.38 0.000 -2.285458 -.873146 _x_20 | -.7283368 .6067298 -1.20 0.230 -1.917505 .4608317 _x_21 | .1640403 .5686955 0.29 0.773 -.9505825 1.278663 _x_22 | -.3893206 .4313133 -0.90 0.367 -1.234679 .4560379 _x_23 | -.6636475 .335296 -1.98 0.048 -1.320816 -.0064793 _x_24 | -.3203404 .3759925 -0.85 0.394 -1.057272 .4165914 _x_25 | .1099051 .4274711 0.26 0.797 -.727923 .9477331 _x_26 | 2.930877 .830572 3.53 0.000 1.302985 4.558768 _x_27 | 1.016372 .2780098 3.66 0.000 .4714826 1.561261 _x_28 | .2754821 .1589062 1.73 0.083 -.0359684 .5869326 _x_29 | .5579576 .1510095 3.69 0.000 .2619843 .8539309 _x_30 | .5383146 .4370587 1.23 0.218 -.3183046 1.394934 _x_31 | .5538315 .7172233 0.77 0.440 -.8519004 1.959563 _x_32 | .3194109 .1238301 2.58 0.010 .0767083 .5621135 _x_33 | -.14026 .4576483 -0.31 0.759 -1.037234 .7567142 _x_34 | .1416171 .3830797 0.37 0.712 -.6092054 .8924396 _x_35 | -.1754525 .3806085 -0.46 0.645 -.9214316 .5705265 _x_36 | 1.376614 .4820982 2.86 0.004 .4317186 2.321509 _x_37 | -.3861 .3466687 -1.11 0.265 -1.065558 .2933581 _x_38 | .5537738 .0982064 5.64 0.000 .3612928 .7462547 _x_39 | .1269701 .4640451 0.27 0.784 -.7825416 1.036482 _x_40 | -.1415814 .7117368 -0.20 0.842 -1.53656 1.253397 _x_41 | .0771879 .2735187 0.28 0.778 -.4588988 .6132747 _x_42 | .4485211 .1064371 4.21 0.000 .2399082 .6571341 _x_43 | 2.957806 1.308001 2.26 0.024 .394172 5.52144 _x_44 | 4.12593 1.241214 3.32 0.001 1.693196 6.558665 _x_45 | 2.223097 .8321762 2.67 0.008 .5920612 3.854132 _x_46 | 2.799832 .8198 3.42 0.001 1.193054 4.406611 _x_47 | -.113412 .7596552 -0.15 0.881 -1.602309 1.375485 _x_48 | -1.341631 1.224874 -1.10 0.273 -3.742339 1.059078 _x_49 | .136736 .8896212 0.15 0.878 -1.60689 1.880361 _x_50 | -.6319624 .8462652 -0.75 0.455 -2.290612 1.026687 _x_51 | -1.495073 1.279023 -1.17 0.242 -4.001913 1.011767 _x_52 | -1.655465 1.212031 -1.37 0.172 -4.031002 .7200729 _x_53 | -.732152 .8824222 -0.83 0.407 -2.461668 .9973637 _x_54 | .0738822 .1811138 0.41 0.683 -.2810943 .4288588 _x_55 | -1.132259 .935811 -1.21 0.226 -2.966415 .7018965 _x_56 | -1.569231 .9276567 -1.69 0.091 -3.387405 .2489426 _x_57 | -1.823517 .871523 -2.09 0.036 -3.531671 -.1153631 _x_58 | -2.308326 .874118 -2.64 0.008 -4.021566 -.5950865 _x_59 | -2.94233 .8551132 -3.44 0.001 -4.618321 -1.266339 _x_60 | -.3838094 .8284513 -0.46 0.643 -2.007544 1.239925 _x_61 | .1468532 .8527383 0.17 0.863 -1.524483 1.81819 _x_62 | .5424095 .8512108 0.64 0.524 -1.125933 2.210752 _x_63 | .7416221 .8877771 0.84 0.404 -.998389 2.481633 _x_64 | 1.592066 .845239 1.88 0.060 -.0645725 3.248704 _x_65 | .779068 .8465486 0.92 0.357 -.8801367 2.438273 _x_66 | 1.329573 .8448083 1.57 0.116 -.3262211 2.985367 _x_67 | .5829629 .4544251 1.28 0.200 -.3076939 1.47362 _x_68 | .0688317 .2791412 0.25 0.805 -.4782751 .6159385 _x_69 | .2654317 .1732888 1.53 0.126 -.0742081 .6050715 _x_70 | -1.391217 .9418014 -1.48 0.140 -3.237114 .4546797 _x_71 | .2337931 .2337582 1.00 0.317 -.2243645 .6919508 _x_72 | -.2462281 .2624685 -0.94 0.348 -.760657 .2682008 _x_73 | -.3552344 .4147618 -0.86 0.392 -1.168153 .4576838 _x_74 | -.1488438 .2257488 -0.66 0.510 -.5913032 .2936157 _x_75 | -.259767 .2635876 -0.99 0.324 -.7763891 .2568552 _x_76 | -1.846626 .8840928 -2.09 0.037 -3.579416 -.1138358 _x_77 | .1634436 .2573096 0.64 0.525 -.3408739 .6677611 _x_78 | 1.112153 .8511606 1.31 0.191 -.556091 2.780397 _x_79 | .0916919 .1910587 0.48 0.631 -.2827762 .4661601 _x_80 | 1.88784 .8642174 2.18 0.029 .1940053 3.581675 _x_81 | 2.931957 .8575471 3.42 0.001 1.251196 4.612718 _x_82 | 2.050644 .8469756 2.42 0.015 .3906025 3.710686 _x_83 | .0673165 .6673819 0.10 0.920 -1.240728 1.375361 _x_84 | 3.878742 .6421816 6.04 0.000 2.620089 5.137395 _x_85 | .6153313 .4550087 1.35 0.176 -.2764693 1.507132 _x_86 | .2403888 .780842 0.31 0.758 -1.290033 1.770811 _x_87 | 4.422627 .6820556 6.48 0.000 3.085822 5.759431 _x_88 | 2.628049 .6309022 4.17 0.000 1.391503 3.864594 _x_89 | -1.503005 .8596189 -1.75 0.080 -3.187827 .1818167 _x_90 | -1.418561 .8478193 -1.67 0.094 -3.080257 .243134 _x_91 | -.9011773 .6977474 -1.29 0.197 -2.268737 .4663826 _x_92 | -.2652421 .6821283 -0.39 0.697 -1.602189 1.071705 _x_93 | .6124922 .9328912 0.66 0.511 -1.215941 2.440925 _x_94 | .1735765 .3034808 0.57 0.567 -.4212348 .7683879 _x_95 | 4.075047 .6939947 5.87 0.000 2.714842 5.435251 _x_96 | -.4341477 .3799067 -1.14 0.253 -1.178751 .3104557 _x_97 | .471427 .444267 1.06 0.289 -.3993203 1.342174 _x_98 | .5873786 .7911568 0.74 0.458 -.9632602 2.138017 _x_99 | -2.810266 .1349743 -20.82 0.000 -3.074811 -2.545722 _x_100 | -1.147101 .2477216 -4.63 0.000 -1.632627 -.6615756 _x_101 | -.4301325 .573272 -0.75 0.453 -1.553725 .69346 _x_102 | -.3816067 .5651922 -0.68 0.500 -1.489363 .7261498 _x_103 | -.55438 .1825699 -3.04 0.002 -.9122103 -.1965496 _x_104 | -.0700637 .4810992 -0.15 0.884 -1.013001 .8728733 _x_105 | -1.403857 .3399079 -4.13 0.000 -2.070064 -.7376493 _x_106 | 4.485583 .8705273 5.15 0.000 2.779381 6.191785 _x_107 | -.5450319 .935811 -0.58 0.560 -2.379188 1.289124 _x_108 | -.6783284 .8786515 -0.77 0.440 -2.400454 1.043797 _x_109 | .9508847 .6445651 1.48 0.140 -.3124397 2.214209 _x_110 | .2466067 .503501 0.49 0.624 -.740237 1.23345 _x_111 | -1.828301 .2344316 -7.80 0.000 -2.287778 -1.368823 _x_112 | -.3122964 .3076899 -1.01 0.310 -.9153575 .2907647 _x_113 | -.7574335 .2621631 -2.89 0.004 -1.271264 -.2436033 _x_114 | 2.404666 .4303943 5.59 0.000 1.561109 3.248223 _x_115 | 3.530798 .2239114 15.77 0.000 3.09194 3.969656 _x_116 | -2.762625 .952315 -2.90 0.004 -4.629128 -.8961215 _x_117 | 1.244483 .3273453 3.80 0.000 .6028979 1.886068 _x_118 | -.6418111 .8532801 -0.75 0.452 -2.314209 1.030587 _x_119 | -.7197791 .9464158 -0.76 0.447 -2.57472 1.135162 _x_120 | -1.111597 .8092821 -1.37 0.170 -2.697761 .4745666 _x_121 | -.4785383 .2732082 -1.75 0.080 -1.014017 .05694 _x_122 | -1.723818 .2141691 -8.05 0.000 -2.143582 -1.304055 _x_123 | .2442174 .3376124 0.72 0.469 -.4174907 .9059254 _x_124 | .0677447 .3317784 0.20 0.838 -.5825291 .7180184 _x_125 | -.1726779 .1694822 -1.02 0.308 -.5048569 .1595012 _x_126 | .4558527 .6894574 0.66 0.508 -.8954589 1.807164 _x_127 | -.593976 .7030128 -0.84 0.398 -1.971856 .7839038 _x_128 | -3.573128 .6378845 -5.60 0.000 -4.823359 -2.322897 _x_129 | 1.831835 .6194486 2.96 0.003 .6177385 3.045932 _x_130 | -1.822488 .3274085 -5.57 0.000 -2.464197 -1.180779 _x_131 | -2.043109 .598021 -3.42 0.001 -3.215208 -.8710094 _x_132 | -.9960651 .6153373 -1.62 0.106 -2.202104 .2099738 _x_133 | -1.012057 .6337938 -1.60 0.110 -2.25427 .2301558 _x_134 | -.7147765 .6181044 -1.16 0.248 -1.926239 .4966858 _x_135 | .6623433 .6427343 1.03 0.303 -.5973927 1.922079 _x_136 | .5181067 .6315897 0.82 0.412 -.7197863 1.756 _x_137 | 1.457936 .5873909 2.48 0.013 .306671 2.609201 _x_138 | 1.695344 .5749411 2.95 0.003 .5684803 2.822208 _x_139 | -.7684451 .5724228 -1.34 0.179 -1.890373 .353483 _x_140 | -.6365124 .5636638 -1.13 0.259 -1.741273 .4682484 _x_141 | .3530931 .2658517 1.33 0.184 -.1679666 .8741528 _x_142 | .58779 .2593568 2.27 0.023 .07946 1.09612 _x_143 | -.2493807 .2981292 -0.84 0.403 -.8337033 .3349419 _x_144 | -.6760089 .2187246 -3.09 0.002 -1.104701 -.2473167 _x_145 | .0837018 .1758878 0.48 0.634 -.261032 .4284356 _x_146 | .4385113 .1799473 2.44 0.015 .0858212 .7912015 _x_147 | 1.031243 .1485008 6.94 0.000 .7401867 1.322299 _x_148 | -.136437 .1497992 -0.91 0.362 -.430038 .1571641 _x_149 | -.1947653 .1793406 -1.09 0.277 -.5462665 .1567358 _x_150 | .6912058 .2024748 3.41 0.001 .2943624 1.088049 _x_151 | -.3852562 .1354994 -2.84 0.004 -.6508302 -.1196823 _x_152 | .4394303 .1539103 2.86 0.004 .1377716 .7410889 _x_153 | .3494945 .5800726 0.60 0.547 -.7874269 1.486416 _x_154 | .606063 .5699171 1.06 0.288 -.5109539 1.72308 _x_155 | .895312 .1246789 7.18 0.000 .6509459 1.139678 _x_156 | 1.193773 .5809485 2.05 0.040 .0551344 2.332411 _x_157 | 1.217784 .5656263 2.15 0.031 .1091768 2.326391 _x_158 | -.0500916 .2834157 -0.18 0.860 -.6055761 .5053929 _cons | -3.25645 .9530785 -3.42 0.001 -5.12445 -1.388451 ------------------------------------------------------------------------------ . poisgof Goodness-of-fit chi2 = 105.607 Prob > chi2(111) = 0.6267 . desmat: poisson count year*meth*mgen year*feth*fgen ethintct*year BW MOh ------------------------------------------------------------------------------- poisson ------------------------------------------------------------------------------- Dependent variable count Number of observations: 225 Initial log likelihood: -2252613.647 Log likelihood: -2267.900 LR chi square: 4500691.495 Model degrees of freedom: 73 Pseudo R-squared: 0.999 Prob: 0.000 ------------------------------------------------------------------------------- nr Effect Coeff s.e. ------------------------------------------------------------------------------- count year 1 80 4.768** 0.369 2 90 5.859** 0.370 meth 3 Mex_Am 2.925** 0.227 4 Oth_H 2.805** 0.230 5 Oth_NH 2.270** 0.264 6 Wht_NH 3.630** 0.213 year.meth 7 80.Mex_Am -0.595* 0.232 8 80.Oth_H -0.026 0.235 9 80.Oth_NH -0.368 0.270 10 80.Wht_NH -1.045** 0.221 11 90.Mex_Am -0.784** 0.233 12 90.Oth_H -0.569* 0.236 13 90.Oth_NH -0.713** 0.271 14 90.Wht_NH -1.450** 0.222 mgen 15 US native 5.023** 0.180 year.mgen 16 80.US native -0.945** 0.186 17 90.US native -1.511** 0.187 meth.mgen 18 Mex_Am.US native -2.956** 0.203 19 Oth_H.US native -2.569** 0.206 20 Oth_NH.US native -2.830** 0.245 21 Wht_NH.US native -0.794** 0.184 year.meth.mgen 22 80.Mex_Am.US native 0.438* 0.210 23 80.Oth_H.US native -0.843** 0.214 24 80.Oth_NH.US native 0.335 0.254 25 80.Wht_NH.US native 0.716** 0.190 26 90.Mex_Am.US native 0.642** 0.211 27 90.Oth_H.US native -0.353 0.215 28 90.Oth_NH.US native 0.754** 0.254 29 90.Wht_NH.US native 1.178** 0.191 feth 30 Mex_Am 4.156** 0.314 31 Oth_H 4.028** 0.317 32 Oth_NH 4.869** 0.313 33 Wht_NH 5.355** 0.302 year.feth 34 80.Mex_Am -1.110** 0.321 35 80.Oth_H -0.187 0.324 36 80.Oth_NH -0.643* 0.320 37 80.Wht_NH -1.362** 0.311 38 90.Mex_Am -1.414** 0.322 39 90.Oth_H -0.763* 0.325 40 90.Oth_NH -1.319** 0.321 41 90.Wht_NH -2.039** 0.312 fgen 42 US native 5.806** 0.268 year.fgen 43 80.US native -1.537** 0.273 44 90.US native -2.018** 0.274 feth.fgen 45 Mex_Am.US native -3.704** 0.285 46 Oth_H.US native -3.201** 0.288 47 Oth_NH.US native -4.596** 0.287 48 Wht_NH.US native -1.906** 0.269 year.feth.fgen 49 80.Mex_Am.US native 1.536** 0.291 50 80.Oth_H.US native -0.090 0.295 51 80.Oth_NH.US native 1.097** 0.294 52 80.Wht_NH.US native 1.605** 0.275 53 90.Mex_Am.US native 1.740** 0.292 54 90.Oth_H.US native 0.204 0.297 55 90.Oth_NH.US native 1.536** 0.295 56 90.Wht_NH.US native 2.046** 0.277 ethintct 57 1 6.418** 0.164 58 2 4.164** 0.111 59 3 3.734** 0.105 60 4 3.586** 0.134 61 5 2.604** 0.093 ethintct.year 62 1.80 -0.608** 0.165 63 1.90 -1.375** 0.165 64 2.80 -0.269* 0.112 65 2.90 -0.564** 0.113 66 3.80 -0.746** 0.109 67 3.90 -0.614** 0.110 68 4.80 -1.032** 0.139 69 4.90 -1.187** 0.140 70 5.80 -0.558** 0.094 71 5.90 -0.819** 0.094 BW 72 1 -1.090** 0.043 MOh 73 1 0.645** 0.042 74 _cons -8.816** 0.361 ------------------------------------------------------------------------------- * p < .05 ** p < .01 . poisgof Goodness-of-fit chi2 = 3203.8 Prob > chi2(151) = 0.0000 . *This is model 8 from HW 3. The model fits fairly poorly, as you can see, an > d the question is how that poor fit would effect something like the standard > errors or coefficients of BW or MOh . desmat year*meth*mgen year*feth*fgen ethintct*year Desmat generated the following design matrix: nr Variables Term Parameterization First Last 1 _x_1 _x_2 year ind(70) 2 _x_3 _x_6 meth ind(1) 3 _x_7 _x_14 year.meth ind(70).ind(1) 4 _x_15 mgen ind(1) 5 _x_16 _x_17 year.mgen ind(70).ind(1) 6 _x_18 _x_21 meth.mgen ind(1).ind(1) 7 _x_22 _x_29 year.meth.mgen ind(70).ind(1).ind(1) 8 _x_30 _x_33 feth ind(1) 9 _x_34 _x_41 year.feth ind(70).ind(1) 10 _x_42 fgen ind(1) 11 _x_43 _x_44 year.fgen ind(70).ind(1) 12 _x_45 _x_48 feth.fgen ind(1).ind(1) 13 _x_49 _x_56 year.feth.fgen ind(70).ind(1).ind(1) 14 _x_57 _x_61 ethintct ind(0) 15 _x_62 _x_71 ethintct.year ind(0).ind(70) . poisson count _x_* BW MOh Iteration 0: log likelihood = -7564925.9 (not concave) Iteration 1: log likelihood = -6357786.7 (not concave) Iteration 2: log likelihood = -6294209.1 (not concave) Iteration 3: log likelihood = -5892242.2 (not concave) Iteration 4: log likelihood = -5710864.6 (not concave) Iteration 5: log likelihood = -5640563.8 (not concave) Iteration 6: log likelihood = -3866424.5 (not concave) Iteration 7: log likelihood = -3712235.8 (not concave) Iteration 8: log likelihood = -3269194.5 (not concave) Iteration 9: log likelihood = -2824350.3 (not concave) Iteration 10: log likelihood = -2629419 (not concave) Iteration 11: log likelihood = -2541632.7 (not concave) Iteration 12: log likelihood = -2457335.8 (not concave) Iteration 13: log likelihood = -2314800.8 (not concave) Iteration 14: log likelihood = -2228330.6 (not concave) Iteration 15: log likelihood = -2115774.4 (not concave) Iteration 16: log likelihood = -2038126.3 (not concave) Iteration 17: log likelihood = -1987516.2 (not concave) Iteration 18: log likelihood = -1787471.8 (not concave) Iteration 19: log likelihood = -1752438.3 (not concave) Iteration 20: log likelihood = -1644093.7 Iteration 21: log likelihood = -1341610.1 (backed up) Iteration 22: log likelihood = -1027365.2 (backed up) Iteration 23: log likelihood = -900973.04 (backed up) Iteration 24: log likelihood = -742266.14 (backed up) Iteration 25: log likelihood = -184580.82 (backed up) Iteration 26: log likelihood = -108044.46 Iteration 27: log likelihood = -52715.285 Iteration 28: log likelihood = -4346.9915 Iteration 29: log likelihood = -2422.6917 Iteration 30: log likelihood = -2273.1195 Iteration 31: log likelihood = -2267.9224 Iteration 32: log likelihood = -2267.9 Iteration 33: log likelihood = -2267.9 Poisson regression Number of obs = 225 LR chi2(73) = 4500691.49 Prob > chi2 = 0.0000 Log likelihood = -2267.9 Pseudo R2 = 0.9990 ------------------------------------------------------------------------------ count | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _x_1 | 4.767666 .368745 12.93 0.000 4.044939 5.490393 _x_2 | 5.858788 .3699615 15.84 0.000 5.133677 6.583899 _x_3 | 2.925455 .2271225 12.88 0.000 2.480303 3.370607 _x_4 | 2.804916 .2295653 12.22 0.000 2.354976 3.254856 _x_5 | 2.270374 .2638028 8.61 0.000 1.75333 2.787418 _x_6 | 3.629811 .2134395 17.01 0.000 3.211477 4.048145 _x_7 | -.5953025 .2321202 -2.56 0.010 -1.05025 -.1403552 _x_8 | -.0260168 .2346668 -0.11 0.912 -.4859553 .4339216 _x_9 | -.3677023 .270495 -1.36 0.174 -.8978627 .1624582 _x_10 | -1.044702 .2206638 -4.73 0.000 -1.477195 -.6122085 _x_11 | -.7843121 .2328548 -3.37 0.001 -1.240699 -.327925 _x_12 | -.5689215 .2356718 -2.41 0.016 -1.03083 -.1070133 _x_13 | -.7133793 .2712111 -2.63 0.009 -1.244943 -.1818153 _x_14 | -1.450126 .2216653 -6.54 0.000 -1.884582 -1.015669 _x_15 | 5.022808 .1801949 27.87 0.000 4.669633 5.375984 _x_16 | -.9449157 .1864832 -5.07 0.000 -1.310416 -.5794152 _x_17 | -1.510767 .1873408 -8.06 0.000 -1.877948 -1.143586 _x_18 | -2.955729 .2030816 -14.55 0.000 -3.353761 -2.557696 _x_19 | -2.568731 .2061698 -12.46 0.000 -2.972816 -2.164645 _x_20 | -2.830474 .2450446 -11.55 0.000 -3.310753 -2.350196 _x_21 | -.7935675 .1836757 -4.32 0.000 -1.153565 -.4335697 _x_22 | .4381241 .21004 2.09 0.037 .0264533 .8497949 _x_23 | -.8434007 .2137725 -3.95 0.000 -1.262387 -.4244142 _x_24 | .3349582 .2536193 1.32 0.187 -.1621266 .8320429 _x_25 | .7157182 .1903597 3.76 0.000 .3426201 1.088816 _x_26 | .6415038 .2107914 3.04 0.002 .2283603 1.054647 _x_27 | -.3530543 .2148303 -1.64 0.100 -.774114 .0680053 _x_28 | .7542539 .2544173 2.96 0.003 .2556052 1.252903 _x_29 | 1.177739 .1913676 6.15 0.000 .8026652 1.552813 _x_30 | 4.156187 .314097 13.23 0.000 3.540569 4.771806 _x_31 | 4.028365 .3168196 12.72 0.000 3.40741 4.64932 _x_32 | 4.86912 .3127991 15.57 0.000 4.256045 5.482195 _x_33 | 5.355022 .3022736 17.72 0.000 4.762577 5.947467 _x_34 | -1.109503 .3213805 -3.45 0.001 -1.739398 -.4796093 _x_35 | -.1871801 .3238449 -0.58 0.563 -.8219044 .4475443 _x_36 | -.6432775 .31987 -2.01 0.044 -1.270211 -.0163439 _x_37 | -1.3625 .3105995 -4.39 0.000 -1.971264 -.753736 _x_38 | -1.41367 .3220179 -4.39 0.000 -2.044814 -.7825269 _x_39 | -.7630081 .3245851 -2.35 0.019 -1.399183 -.1268329 _x_40 | -1.319386 .3207658 -4.11 0.000 -1.948076 -.6906968 _x_41 | -2.03857 .3115365 -6.54 0.000 -2.64917 -1.427969 _x_42 | 5.806197 .2676627 21.69 0.000 5.281588 6.330806 _x_43 | -1.536591 .2730208 -5.63 0.000 -2.071702 -1.00148 _x_44 | -2.018335 .2743758 -7.36 0.000 -2.556101 -1.480568 _x_45 | -3.704223 .2845438 -13.02 0.000 -4.261918 -3.146527 _x_46 | -3.201114 .2883958 -11.10 0.000 -3.766359 -2.635869 _x_47 | -4.595846 .2870143 -16.01 0.000 -5.158384 -4.033309 _x_48 | -1.906128 .269368 -7.08 0.000 -2.43408 -1.378177 _x_49 | 1.535704 .2911845 5.27 0.000 .9649932 2.106415 _x_50 | -.0895802 .2951977 -0.30 0.762 -.6681571 .4889966 _x_51 | 1.096716 .2936134 3.74 0.000 .5212443 1.672187 _x_52 | 1.604594 .2750344 5.83 0.000 1.065537 2.143651 _x_53 | 1.740488 .2924411 5.95 0.000 1.167314 2.313663 _x_54 | .2043723 .296518 0.69 0.491 -.3767924 .7855369 _x_55 | 1.536076 .2951378 5.20 0.000 .9576165 2.114535 _x_56 | 2.046135 .2765219 7.40 0.000 1.504162 2.588108 _x_57 | 6.417975 .1644435 39.03 0.000 6.095672 6.740279 _x_58 | 4.163822 .1108055 37.58 0.000 3.946648 4.380997 _x_59 | 3.734246 .1054168 35.42 0.000 3.527633 3.940859 _x_60 | 3.585972 .133858 26.79 0.000 3.323615 3.848328 _x_61 | 2.603656 .0930337 27.99 0.000 2.421314 2.785999 _x_62 | -.607971 .165434 -3.68 0.000 -.9322158 -.2837262 _x_63 | -1.37477 .1652673 -8.32 0.000 -1.698688 -1.050852 _x_64 | -.2694077 .1123403 -2.40 0.016 -.4895907 -.0492246 _x_65 | -.5639752 .1127743 -5.00 0.000 -.7850087 -.3429418 _x_66 | -.7463645 .1089126 -6.85 0.000 -.9598294 -.5328997 _x_67 | -.6142567 .1097559 -5.60 0.000 -.8293742 -.3991392 _x_68 | -1.031803 .1387407 -7.44 0.000 -1.30373 -.7598762 _x_69 | -1.187 .1396957 -8.50 0.000 -1.460798 -.9132012 _x_70 | -.5578863 .0936511 -5.96 0.000 -.741439 -.3743335 _x_71 | -.8186344 .094049 -8.70 0.000 -1.002967 -.6343019 BW | -1.08981 .0433593 -25.13 0.000 -1.174793 -1.004828 MOh | .6447441 .0421509 15.30 0.000 .5621298 .7273584 _cons | -8.81557 .3613183 -24.40 0.000 -9.523741 -8.107399 ------------------------------------------------------------------------------ . poisgof Goodness-of-fit chi2 = 3203.8 Prob > chi2(151) = 0.0000 . poisson count _x_* BW MOh, robust Iteration 0: log likelihood = -7564925.9 (not concave) Iteration 1: log likelihood = -6357786.7 (not concave) Iteration 2: log likelihood = -6294209.1 (not concave) Iteration 3: log likelihood = -5892242.2 (not concave) Iteration 4: log likelihood = -5710864.6 (not concave) Iteration 5: log likelihood = -5640563.8 (not concave) Iteration 6: log likelihood = -3866424.5 (not concave) Iteration 7: log likelihood = -3712235.8 (not concave) Iteration 8: log likelihood = -3269194.5 (not concave) Iteration 9: log likelihood = -2824350.3 (not concave) Iteration 10: log likelihood = -2629419 (not concave) Iteration 11: log likelihood = -2541632.7 (not concave) Iteration 12: log likelihood = -2457335.8 (not concave) Iteration 13: log likelihood = -2314800.8 (not concave) Iteration 14: log likelihood = -2228330.6 (not concave) Iteration 15: log likelihood = -2115774.4 (not concave) Iteration 16: log likelihood = -2038126.3 (not concave) Iteration 17: log likelihood = -1987516.2 (not concave) Iteration 18: log likelihood = -1787471.8 (not concave) Iteration 19: log likelihood = -1752438.3 (not concave) Iteration 20: log likelihood = -1644093.7 Iteration 21: log likelihood = -1341610.1 (backed up) Iteration 22: log likelihood = -1027365.2 (backed up) Iteration 23: log likelihood = -900973.04 (backed up) Iteration 24: log likelihood = -742266.14 (backed up) Iteration 25: log likelihood = -184580.82 (backed up) Iteration 26: log likelihood = -108044.46 Iteration 27: log likelihood = -52715.285 Iteration 28: log likelihood = -4346.9915 Iteration 29: log likelihood = -2422.6917 Iteration 30: log likelihood = -2273.1195 Iteration 31: log likelihood = -2267.9224 Iteration 32: log likelihood = -2267.9 Iteration 33: log likelihood = -2267.9 Poisson regression Number of obs = 225 Wald chi2(73) = 6404395.51 Prob > chi2 = 0.0000 Log likelihood = -2267.9 Pseudo R2 = 0.9990 ------------------------------------------------------------------------------ | Robust count | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _x_1 | 4.767666 .4713681 10.11 0.000 3.843801 5.69153 _x_2 | 5.858788 .5001636 11.71 0.000 4.878485 6.839091 _x_3 | 2.925455 .2965029 9.87 0.000 2.34432 3.50659 _x_4 | 2.804916 .6402485 4.38 0.000 1.550052 4.05978 _x_5 | 2.270374 .4160722 5.46 0.000 1.454888 3.085861 _x_6 | 3.629811 .3108912 11.68 0.000 3.020475 4.239146 _x_7 | -.5953025 .3265084 -1.82 0.068 -1.235247 .0446423 _x_8 | -.0260168 .6546395 -0.04 0.968 -1.309087 1.257053 _x_9 | -.3677023 .4820566 -0.76 0.446 -1.312516 .5771112 _x_10 | -1.044702 .3509188 -2.98 0.003 -1.73249 -.3569136 _x_11 | -.7843121 .3339589 -2.35 0.019 -1.438859 -.1297647 _x_12 | -.5689215 .6537292 -0.87 0.384 -1.850207 .7123641 _x_13 | -.7133793 .4625508 -1.54 0.123 -1.619962 .1932036 _x_14 | -1.450126 .3508565 -4.13 0.000 -2.137792 -.7624594 _x_15 | 5.022808 .2089995 24.03 0.000 4.613177 5.43244 _x_16 | -.9449157 .236461 -4.00 0.000 -1.408371 -.4814606 _x_17 | -1.510767 .246582 -6.13 0.000 -1.994059 -1.027475 _x_18 | -2.955729 .224135 -13.19 0.000 -3.395025 -2.516432 _x_19 | -2.568731 .5837248 -4.40 0.000 -3.712811 -1.424651 _x_20 | -2.830474 .3754738 -7.54 0.000 -3.56639 -2.094559 _x_21 | -.7935675 .2105512 -3.77 0.000 -1.20624 -.3808949 _x_22 | .4381241 .2575472 1.70 0.089 -.0666591 .9429073 _x_23 | -.8434007 .5998957 -1.41 0.160 -2.019175 .3323733 _x_24 | .3349582 .4590528 0.73 0.466 -.5647688 1.234685 _x_25 | .7157182 .2392331 2.99 0.003 .2468299 1.184606 _x_26 | .6415038 .2716937 2.36 0.018 .108994 1.174014 _x_27 | -.3530543 .601014 -0.59 0.557 -1.53102 .8249114 _x_28 | .7542539 .4406997 1.71 0.087 -.1095017 1.61801 _x_29 | 1.177739 .2491619 4.73 0.000 .6893905 1.666087 _x_30 | 4.156187 .3228987 12.87 0.000 3.523318 4.789057 _x_31 | 4.028365 .8871587 4.54 0.000 2.289566 5.767164 _x_32 | 4.86912 .6086855 8.00 0.000 3.676119 6.062122 _x_33 | 5.355022 .3120675 17.16 0.000 4.743381 5.966663 _x_34 | -1.109503 .3712398 -2.99 0.003 -1.83712 -.3818869 _x_35 | -.1871801 .9114194 -0.21 0.837 -1.973529 1.599169 _x_36 | -.6432775 .7023515 -0.92 0.360 -2.019861 .7333062 _x_37 | -1.3625 .3795815 -3.59 0.000 -2.106466 -.6185338 _x_38 | -1.41367 .4109438 -3.44 0.001 -2.219105 -.6082353 _x_39 | -.7630081 .9210979 -0.83 0.407 -2.568327 1.042311 _x_40 | -1.319386 .6999072 -1.89 0.059 -2.691179 .0524067 _x_41 | -2.03857 .4094784 -4.98 0.000 -2.841132 -1.236007 _x_42 | 5.806197 .1633222 35.55 0.000 5.486092 6.126303 _x_43 | -1.536591 .1707847 -9.00 0.000 -1.871323 -1.201859 _x_44 | -2.018335 .242934 -8.31 0.000 -2.494476 -1.542193 _x_45 | -3.704223 .2217433 -16.71 0.000 -4.138832 -3.269614 _x_46 | -3.201114 .8213186 -3.90 0.000 -4.810869 -1.591359 _x_47 | -4.595846 .5745366 -8.00 0.000 -5.721917 -3.469775 _x_48 | -1.906128 .1642143 -11.61 0.000 -2.227982 -1.584274 _x_49 | 1.535704 .2310118 6.65 0.000 1.082929 1.988479 _x_50 | -.0895802 .8330585 -0.11 0.914 -1.722345 1.543184 _x_51 | 1.096716 .6735027 1.63 0.103 -.2233252 2.416757 _x_52 | 1.604594 .1746425 9.19 0.000 1.262301 1.946887 _x_53 | 1.740488 .2922028 5.96 0.000 1.167781 2.313195 _x_54 | .2043723 .8429354 0.24 0.808 -1.447751 1.856495 _x_55 | 1.536076 .663722 2.31 0.021 .2352048 2.836947 _x_56 | 2.046135 .2450515 8.35 0.000 1.565843 2.526428 _x_57 | 6.417975 .314938 20.38 0.000 5.800708 7.035242 _x_58 | 4.163822 .186249 22.36 0.000 3.798781 4.528864 _x_59 | 3.734246 .2680174 13.93 0.000 3.208941 4.25955 _x_60 | 3.585972 .3394308 10.56 0.000 2.920699 4.251244 _x_61 | 2.603656 .1848853 14.08 0.000 2.241288 2.966025 _x_62 | -.607971 .354952 -1.71 0.087 -1.303664 .0877221 _x_63 | -1.37477 .3316263 -4.15 0.000 -2.024746 -.7247947 _x_64 | -.2694077 .1977155 -1.36 0.173 -.656923 .1181076 _x_65 | -.5639752 .198092 -2.85 0.004 -.9522285 -.175722 _x_66 | -.7463645 .2880131 -2.59 0.010 -1.31086 -.1818692 _x_67 | -.6142567 .2751613 -2.23 0.026 -1.153563 -.0749504 _x_68 | -1.031803 .4801827 -2.15 0.032 -1.972944 -.0906622 _x_69 | -1.187 .4514856 -2.63 0.009 -2.071895 -.3021043 _x_70 | -.5578863 .1975343 -2.82 0.005 -.9450465 -.1707261 _x_71 | -.8186344 .1921043 -4.26 0.000 -1.195152 -.442117 BW | -1.08981 .1356693 -8.03 0.000 -1.355717 -.8239035 MOh | .6447441 .1206303 5.34 0.000 .4083131 .8811751 _cons | -8.81557 .424392 -20.77 0.000 -9.647363 -7.983777 ------------------------------------------------------------------------------ . poisgof Goodness-of-fit chi2 = 3203.8 Prob > chi2(151) = 0.0000 . *The SEs of the BW term and the MOh term are 3 times larger if you use robust > SE, though the coefficients are still significant. . nbreg count _x_* BW MOh Fitting comparison Poisson model: Iteration 0: log likelihood = -7564925.9 (not concave) Iteration 1: log likelihood = -6357786.7 (not concave) Iteration 2: log likelihood = -6294209.1 (not concave) Iteration 3: log likelihood = -5892242.2 (not concave) Iteration 4: log likelihood = -5710864.6 (not concave) Iteration 5: log likelihood = -5640563.8 (not concave) Iteration 6: log likelihood = -3866424.5 (not concave) Iteration 7: log likelihood = -3712235.8 (not concave) Iteration 8: log likelihood = -3269194.5 (not concave) Iteration 9: log likelihood = -2824350.3 (not concave) Iteration 10: log likelihood = -2629419 (not concave) Iteration 11: log likelihood = -2541632.7 (not concave) Iteration 12: log likelihood = -2457335.8 (not concave) Iteration 13: log likelihood = -2314800.8 (not concave) Iteration 14: log likelihood = -2228330.6 (not concave) Iteration 15: log likelihood = -2115774.4 (not concave) Iteration 16: log likelihood = -2038126.3 (not concave) Iteration 17: log likelihood = -1987516.2 (not concave) Iteration 18: log likelihood = -1787471.8 (not concave) Iteration 19: log likelihood = -1752438.3 (not concave) Iteration 20: log likelihood = -1644093.7 Iteration 21: log likelihood = -1341610.1 (backed up) Iteration 22: log likelihood = -1027365.2 (backed up) Iteration 23: log likelihood = -900973.04 (backed up) Iteration 24: log likelihood = -742266.14 (backed up) Iteration 25: log likelihood = -184580.82 (backed up) Iteration 26: log likelihood = -108044.46 Iteration 27: log likelihood = -52715.285 Iteration 28: log likelihood = -4346.9915 Iteration 29: log likelihood = -2422.6917 Iteration 30: log likelihood = -2273.1195 Iteration 31: log likelihood = -2267.9224 Iteration 32: log likelihood = -2267.9 Iteration 33: log likelihood = -2267.9 Fitting constant-only model: Iteration 0: log likelihood = -2017.9181 Iteration 1: log likelihood = -1551.9413 Iteration 2: log likelihood = -1551.3525 Iteration 3: log likelihood = -1551.3519 Iteration 4: log likelihood = -1551.3519 Fitting full model: Iteration 0: log likelihood = -1511.6146 (not concave) Iteration 1: log likelihood = -1408.9858 (not concave) Iteration 2: log likelihood = -1305.2166 (not concave) Iteration 3: log likelihood = -1175.5532 (not concave) Iteration 4: log likelihood = -1116.6027 Iteration 5: log likelihood = -1071.1488 Iteration 6: log likelihood = -1067.3466 Iteration 7: log likelihood = -1067.3389 Iteration 8: log likelihood = -1067.3389 Negative binomial regression Number of obs = 225 LR chi2(73) = 968.03 Prob > chi2 = 0.0000 Log likelihood = -1067.3389 Pseudo R2 = 0.3120 ------------------------------------------------------------------------------ count | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _x_1 | 4.7163 .8457022 5.58 0.000 3.058755 6.373846 _x_2 | 6.085512 .8263569 7.36 0.000 4.465882 7.705142 _x_3 | 2.038866 .5135944 3.97 0.000 1.032239 3.045492 _x_4 | 2.487975 .5107553 4.87 0.000 1.486913 3.489037 _x_5 | 1.570823 .5301022 2.96 0.003 .5318418 2.609804 _x_6 | 3.490303 .5039885 6.93 0.000 2.502503 4.478102 _x_7 | -.1781115 .5762705 -0.31 0.757 -1.307581 .9513579 _x_8 | .0524976 .5735161 0.09 0.927 -1.071573 1.176569 _x_9 | -.0068089 .5959011 -0.01 0.991 -1.174754 1.161136 _x_10 | -.8640328 .5770101 -1.50 0.134 -1.994952 .2668862 _x_11 | -.4244137 .5699466 -0.74 0.456 -1.541489 .6926612 _x_12 | -.6349301 .5688704 -1.12 0.264 -1.749896 .4800353 _x_13 | -.431658 .5901504 -0.73 0.465 -1.588332 .7250155 _x_14 | -1.424094 .5725695 -2.49 0.013 -2.546309 -.3018782 _x_15 | 4.16903 .4305608 9.68 0.000 3.325146 5.012913 _x_16 | -.4825728 .4946473 -0.98 0.329 -1.452064 .4869181 _x_17 | -1.198732 .4892726 -2.45 0.014 -2.157688 -.2397749 _x_18 | -2.429208 .5093075 -4.77 0.000 -3.427432 -1.430983 _x_19 | -2.146872 .5085962 -4.22 0.000 -3.143702 -1.150042 _x_20 | -2.373074 .5327767 -4.45 0.000 -3.417297 -1.328851 _x_21 | -.5802743 .5019443 -1.16 0.248 -1.564067 .4035185 _x_22 | .1232114 .5989994 0.21 0.837 -1.050806 1.297229 _x_23 | -1.072979 .5975433 -1.80 0.073 -2.244142 .0981848 _x_24 | -.0104703 .6225908 -0.02 0.987 -1.230726 1.209785 _x_25 | .3073225 .5905468 0.52 0.603 -.850128 1.464773 _x_26 | .4287588 .593855 0.72 0.470 -.7351756 1.592693 _x_27 | -.1584982 .5928444 -0.27 0.789 -1.320452 1.003455 _x_28 | .5388215 .6174358 0.87 0.383 -.6713305 1.748973 _x_29 | .9959014 .5871735 1.70 0.090 -.1549375 2.14674 _x_30 | 3.707552 .6102813 6.08 0.000 2.511422 4.903681 _x_31 | 4.070499 .6085438 6.69 0.000 2.877775 5.263222 _x_32 | 4.281758 .5994997 7.14 0.000 3.10676 5.456755 _x_33 | 5.360327 .5975156 8.97 0.000 4.189218 6.531436 _x_34 | -1.725268 .6857432 -2.52 0.012 -3.0693 -.381236 _x_35 | -1.181811 .6809483 -1.74 0.083 -2.516445 .1528231 _x_36 | -1.021759 .6737364 -1.52 0.129 -2.342258 .2987402 _x_37 | -1.63197 .6795936 -2.40 0.016 -2.963949 -.2999905 _x_38 | -2.128245 .667787 -3.19 0.001 -3.437083 -.8194065 _x_39 | -1.921397 .6652091 -2.89 0.004 -3.225183 -.6176107 _x_40 | -1.931294 .6571017 -2.94 0.003 -3.21919 -.6433986 _x_41 | -2.693648 .6645409 -4.05 0.000 -3.996124 -1.391172 _x_42 | 5.173301 .510437 10.14 0.000 4.172862 6.173739 _x_43 | -1.641487 .5877086 -2.79 0.005 -2.793375 -.4895993 _x_44 | -2.438976 .5723691 -4.26 0.000 -3.560798 -1.317153 _x_45 | -3.278937 .5808252 -5.65 0.000 -4.417334 -2.140541 _x_46 | -2.898389 .5856981 -4.95 0.000 -4.046336 -1.750442 _x_47 | -4.206857 .5834991 -7.21 0.000 -5.350494 -3.063219 _x_48 | -1.809435 .5667512 -3.19 0.001 -2.920247 -.6986232 _x_49 | 1.967181 .681143 2.89 0.004 .6321648 3.302196 _x_50 | .3485593 .6828454 0.51 0.610 -.989793 1.686912 _x_51 | 1.378791 .6831498 2.02 0.044 .0398416 2.71774 _x_52 | 1.523307 .6655981 2.29 0.022 .2187589 2.827856 _x_53 | 2.350373 .6666751 3.53 0.000 1.043714 3.657032 _x_54 | 1.065679 .6694803 1.59 0.111 -.2464783 2.377836 _x_55 | 2.13 .6689354 3.18 0.001 .8189105 3.441089 _x_56 | 2.402781 .6530887 3.68 0.000 1.122751 3.682811 _x_57 | 5.911581 .4076022 14.50 0.000 5.112695 6.710466 _x_58 | 4.262244 .3338974 12.77 0.000 3.607817 4.916671 _x_59 | 2.599357 .3327315 7.81 0.000 1.947216 3.251499 _x_60 | 3.548045 .3509065 10.11 0.000 2.860281 4.235809 _x_61 | 2.00936 .3043265 6.60 0.000 1.412891 2.605829 _x_62 | -.8314368 .4778225 -1.74 0.082 -1.767952 .105078 _x_63 | -1.542221 .4753148 -3.24 0.001 -2.473821 -.6106211 _x_64 | -.2577752 .4152559 -0.62 0.535 -1.071662 .5561114 _x_65 | -.5491291 .4134831 -1.33 0.184 -1.359541 .2612829 _x_66 | .163102 .4150908 0.39 0.694 -.6504611 .976665 _x_67 | .1065479 .4136882 0.26 0.797 -.704266 .9173619 _x_68 | -1.172415 .445193 -2.63 0.008 -2.044978 -.2998531 _x_69 | -1.231835 .4431854 -2.78 0.005 -2.100462 -.3632074 _x_70 | -.3230673 .3927721 -0.82 0.411 -1.092887 .4467519 _x_71 | -.4683423 .3922281 -1.19 0.232 -1.237095 .3004107 BW | -.870227 .1572083 -5.54 0.000 -1.17835 -.5621045 MOh | .6452285 .1486543 4.34 0.000 .3538715 .9365855 _cons | -7.269718 .7741349 -9.39 0.000 -8.786995 -5.752442 -------------+---------------------------------------------------------------- /lnalpha | -2.120454 .1219087 -2.359391 -1.881517 -------------+---------------------------------------------------------------- alpha | .1199771 .0146263 .0944778 .1523587 ------------------------------------------------------------------------------ Likelihood ratio test of alpha=0: chibar2(01) = 2401.12 Prob>=chibar2 = 0.000 . *This is the nbreg version of model 8, hw 3. First thing to notice is that A > lpha, the over dispersion parameter is highly significant, which is not surpr > ising since Model 8 fit poorly. The next question is how does nbreg effect t > he SE of BW and MOh. . *The answer is that it inflates them even more than robust SE with poisson re > gression did. . *It is also possible to add robust SE to nbreg . nbreg count _x_* BW MOh, robust Getting starting values from Poisson model: Iteration 0: log likelihood = -7564925.9 (not concave) Iteration 1: log likelihood = -6357786.7 (not concave) Iteration 2: log likelihood = -6294209.1 (not concave) Iteration 3: log likelihood = -5892242.2 (not concave) Iteration 4: log likelihood = -5710864.6 (not concave) Iteration 5: log likelihood = -5640563.8 (not concave) Iteration 6: log likelihood = -3866424.5 (not concave) Iteration 7: log likelihood = -3712235.8 (not concave) Iteration 8: log likelihood = -3269194.5 (not concave) Iteration 9: log likelihood = -2824350.3 (not concave) Iteration 10: log likelihood = -2629419 (not concave) Iteration 11: log likelihood = -2541632.7 (not concave) Iteration 12: log likelihood = -2457335.8 (not concave) Iteration 13: log likelihood = -2314800.8 (not concave) Iteration 14: log likelihood = -2228330.6 (not concave) Iteration 15: log likelihood = -2115774.4 (not concave) Iteration 16: log likelihood = -2038126.3 (not concave) Iteration 17: log likelihood = -1987516.2 (not concave) Iteration 18: log likelihood = -1787471.8 (not concave) Iteration 19: log likelihood = -1752438.3 (not concave) Iteration 20: log likelihood = -1644093.7 Iteration 21: log likelihood = -1341610.1 (backed up) Iteration 22: log likelihood = -1027365.2 (backed up) Iteration 23: log likelihood = -900973.04 (backed up) Iteration 24: log likelihood = -742266.14 (backed up) Iteration 25: log likelihood = -184580.82 (backed up) Iteration 26: log likelihood = -108044.46 Iteration 27: log likelihood = -52715.285 Iteration 28: log likelihood = -4346.9915 Iteration 29: log likelihood = -2422.6917 Iteration 30: log likelihood = -2273.1195 Iteration 31: log likelihood = -2267.9224 Iteration 32: log likelihood = -2267.9 Iteration 33: log likelihood = -2267.9 Fitting constant-only model: Iteration 0: log likelihood = -2017.9181 Iteration 1: log likelihood = -1551.9413 Iteration 2: log likelihood = -1551.3525 Iteration 3: log likelihood = -1551.3519 Iteration 4: log likelihood = -1551.3519 Fitting full model: Iteration 0: log likelihood = -1511.6146 (not concave) Iteration 1: log likelihood = -1408.9858 (not concave) Iteration 2: log likelihood = -1305.2166 (not concave) Iteration 3: log likelihood = -1175.5532 (not concave) Iteration 4: log likelihood = -1116.6027 Iteration 5: log likelihood = -1071.1488 Iteration 6: log likelihood = -1067.3466 Iteration 7: log likelihood = -1067.3389 Iteration 8: log likelihood = -1067.3389 Negative binomial regression Number of obs = 225 Wald chi2(73) = 17056.66 Prob > chi2 = 0.0000 Log likelihood = -1067.3389 Pseudo R2 = 0.3120 ------------------------------------------------------------------------------ | Robust count | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _x_1 | 4.7163 1.129029 4.18 0.000 2.503444 6.929157 _x_2 | 6.085512 1.147493 5.30 0.000 3.836467 8.334557 _x_3 | 2.038866 .7215179 2.83 0.005 .6247165 3.453015 _x_4 | 2.487975 .7589699 3.28 0.001 1.000421 3.975528 _x_5 | 1.570823 .794292 1.98 0.048 .0140392 3.127607 _x_6 | 3.490303 .7277513 4.80 0.000 2.063936 4.916669 _x_7 | -.1781115 .7567784 -0.24 0.814 -1.66137 1.305147 _x_8 | .0524976 .8029204 0.07 0.948 -1.521197 1.626193 _x_9 | -.0068089 .8161556 -0.01 0.993 -1.606445 1.592827 _x_10 | -.8640328 .7519289 -1.15 0.251 -2.337786 .6097208 _x_11 | -.4244137 .7391854 -0.57 0.566 -1.873191 1.024363 _x_12 | -.6349301 .7789458 -0.82 0.415 -2.161636 .8917757 _x_13 | -.431658 .8036013 -0.54 0.591 -2.006688 1.143372 _x_14 | -1.424094 .7453401 -1.91 0.056 -2.884934 .0367458 _x_15 | 4.16903 .6834132 6.10 0.000 2.829564 5.508495 _x_16 | -.4825728 .7130975 -0.68 0.499 -1.880218 .9150725 _x_17 | -1.198732 .7064433 -1.70 0.090 -2.583335 .1858719 _x_18 | -2.429208 .6993481 -3.47 0.001 -3.799905 -1.058511 _x_19 | -2.146872 .7709917 -2.78 0.005 -3.657988 -.6357557 _x_20 | -2.373074 .7739279 -3.07 0.002 -3.889945 -.8562032 _x_21 | -.5802743 .7168051 -0.81 0.418 -1.985186 .8246379 _x_22 | .1232114 .7554472 0.16 0.870 -1.357438 1.603861 _x_23 | -1.072979 .8316457 -1.29 0.197 -2.702974 .5570172 _x_24 | -.0104703 .8272104 -0.01 0.990 -1.631773 1.610832 _x_25 | .3073225 .7574239 0.41 0.685 -1.177201 1.791846 _x_26 | .4287588 .7388517 0.58 0.562 -1.019364 1.876881 _x_27 | -.1584982 .8111434 -0.20 0.845 -1.74831 1.431314 _x_28 | .5388215 .8130851 0.66 0.508 -1.054796 2.132439 _x_29 | .9959014 .7494741 1.33 0.184 -.4730409 2.464844 _x_30 | 3.707552 .6906329 5.37 0.000 2.353936 5.061167 _x_31 | 4.070499 .7706597 5.28 0.000 2.560033 5.580964 _x_32 | 4.281758 .7457058 5.74 0.000 2.820201 5.743314 _x_33 | 5.360327 .7016152 7.64 0.000 3.985186 6.735467 _x_34 | -1.725268 .7380544 -2.34 0.019 -3.171828 -.278708 _x_35 | -1.181811 .8112263 -1.46 0.145 -2.771785 .4081632 _x_36 | -1.021759 .8080136 -1.26 0.206 -2.605437 .5619186 _x_37 | -1.63197 .7707799 -2.12 0.034 -3.14267 -.1212687 _x_38 | -2.128245 .7703446 -2.76 0.006 -3.638092 -.6183972 _x_39 | -1.921397 .8376383 -2.29 0.022 -3.563138 -.2796558 _x_40 | -1.931294 .8270917 -2.34 0.020 -3.552364 -.3102243 _x_41 | -2.693648 .8085041 -3.33 0.001 -4.278287 -1.109009 _x_42 | 5.173301 .5729871 9.03 0.000 4.050267 6.296335 _x_43 | -1.641487 .6571476 -2.50 0.012 -2.929473 -.3535014 _x_44 | -2.438976 .7035169 -3.47 0.001 -3.817843 -1.060108 _x_45 | -3.278937 .5961974 -5.50 0.000 -4.447463 -2.110412 _x_46 | -2.898389 .7775773 -3.73 0.000 -4.422412 -1.374366 _x_47 | -4.206857 .7187562 -5.85 0.000 -5.615593 -2.79812 _x_48 | -1.809435 .5972983 -3.03 0.002 -2.980118 -.6387519 _x_49 | 1.967181 .6864331 2.87 0.004 .6217963 3.312565 _x_50 | .3485593 .8454104 0.41 0.680 -1.308415 2.005533 _x_51 | 1.378791 .8353331 1.65 0.099 -.2584323 3.016013 _x_52 | 1.523307 .70424 2.16 0.031 .1430222 2.903592 _x_53 | 2.350373 .727799 3.23 0.001 .9239134 3.776833 _x_54 | 1.065679 .8761972 1.22 0.224 -.651636 2.782994 _x_55 | 2.13 .8484682 2.51 0.012 .4670325 3.792967 _x_56 | 2.402781 .7459509 3.22 0.001 .9407441 3.864818 _x_57 | 5.911581 .5591443 10.57 0.000 4.815678 7.007483 _x_58 | 4.262244 .2828271 15.07 0.000 3.707913 4.816575 _x_59 | 2.599357 .5597417 4.64 0.000 1.502284 3.696431 _x_60 | 3.548045 .5131409 6.91 0.000 2.542307 4.553782 _x_61 | 2.00936 .2906312 6.91 0.000 1.439734 2.578987 _x_62 | -.8314368 .6195509 -1.34 0.180 -2.045734 .3828606 _x_63 | -1.542221 .6382693 -2.42 0.016 -2.793206 -.2912361 _x_64 | -.2577752 .3162034 -0.82 0.415 -.8775225 .3619721 _x_65 | -.5491291 .2986513 -1.84 0.066 -1.134475 .0362167 _x_66 | .163102 .5825786 0.28 0.780 -.9787312 1.304935 _x_67 | .1065479 .5672189 0.19 0.851 -1.005181 1.218277 _x_68 | -1.172415 .6829178 -1.72 0.086 -2.51091 .1660789 _x_69 | -1.231835 .6405804 -1.92 0.054 -2.487349 .0236798 _x_70 | -.3230673 .3706238 -0.87 0.383 -1.049477 .4033419 _x_71 | -.4683423 .3517988 -1.33 0.183 -1.157855 .2211708 BW | -.870227 .1676956 -5.19 0.000 -1.198904 -.5415497 MOh | .6452285 .1211096 5.33 0.000 .4078582 .8825989 _cons | -7.269718 1.108163 -6.56 0.000 -9.441679 -5.097758 -------------+---------------------------------------------------------------- /lnalpha | -2.120454 .1279129 -2.371159 -1.869749 -------------+---------------------------------------------------------------- alpha | .1199771 .0153466 .0933725 .1541623 ------------------------------------------------------------------------------ . *Here one sees that the SE of the BW term is largest, 0.167, but the SE of MO > h is a bit smaller than it was with nbreg and ordinary standard errors. Ther > e is no certain direction of the changes, though generally poisson regression > with ordinary standard errors will have standard errors that are too small i > f the model doesn't fit well. . exit, clear