------------------------------------------------------------------------------- log: c:\bill\econ626\abortion_example.log log type: text opened on: 10 Dec 2006, 09:01:44 . * generate the abortion rate, which; . * is the fraction of pregnancies that; . * end in abortion; . gen abort_rate=abortions/(births+abortions); . gen birthsl=log(births); . * look at distribution of infant homicide; . * counts per year; . tab homicides; infant | murders | Freq. Percent Cum. ------------+----------------------------------- 0 | 290 24.72 24.72 1 | 149 12.70 37.43 2 | 128 10.91 48.34 3 | 103 8.78 57.12 4 | 89 7.59 64.71 5 | 70 5.97 70.67 6 | 52 4.43 75.11 7 | 49 4.18 79.28 8 | 46 3.92 83.21 9 | 32 2.73 85.93 10 | 30 2.56 88.49 11 | 13 1.11 89.60 12 | 14 1.19 90.79 13 | 14 1.19 91.99 14 | 5 0.43 92.41 15 | 19 1.62 94.03 16 | 6 0.51 94.54 17 | 5 0.43 94.97 18 | 5 0.43 95.40 19 | 5 0.43 95.82 20 | 3 0.26 96.08 21 | 3 0.26 96.33 22 | 4 0.34 96.68 23 | 3 0.26 96.93 24 | 5 0.43 97.36 25 | 4 0.34 97.70 26 | 2 0.17 97.87 28 | 2 0.17 98.04 29 | 2 0.17 98.21 30 | 1 0.09 98.29 33 | 2 0.17 98.47 34 | 1 0.09 98.55 35 | 1 0.09 98.64 36 | 1 0.09 98.72 37 | 2 0.17 98.89 39 | 2 0.17 99.06 40 | 1 0.09 99.15 42 | 1 0.09 99.23 44 | 2 0.17 99.40 45 | 1 0.09 99.49 46 | 2 0.17 99.66 49 | 1 0.09 99.74 53 | 1 0.09 99.83 56 | 1 0.09 99.91 58 | 1 0.09 100.00 ------------+----------------------------------- Total | 1,173 100.00 . * look at distribution of abort_rate; . sum abort_rate, detail; abort_rate ------------------------------------------------------------- Percentiles Smallest 1% .0683419 .016592 5% .0959279 .0221789 10% .1202337 .0304086 Obs 1173 25% .1779262 .0307039 Sum of Wgt. 1173 50% .2340892 Mean .2499488 Largest Std. Dev. .117977 75% .3025208 .7551766 90% .3686219 .7567868 Variance .0139186 95% .4215921 .7689381 Skewness 1.617038 99% .7055163 .8967241 Kurtosis 7.535949 . * generate year and state effects; . xi i.year i.stfip; i.year _Iyear_1978-2000 (naturally coded; _Iyear_1978 omitted) i.stfip _Istfip_1-56 (naturally coded; _Istfip_1 omitted) . * get local list for right hand side variables; . local xlist1 abort_rate birthsl popdensity _Iy*; . local xlist2 abort_rate birthsl popdensity _Iy* _Ist*; . * run a fixed-effect poisson model; . * the conditioning variable is stfip; . * fe is the option for fixed effects; . * can also ask for random effects; . xtpoisson homicide `xlist1', i(stfip) fe; Iteration 0: log likelihood = -2329.0171 Iteration 1: log likelihood = -2242.9876 Iteration 2: log likelihood = -2241.6838 Iteration 3: log likelihood = -2241.6767 Iteration 4: log likelihood = -2241.6767 Conditional fixed-effects Poisson regression Number of obs = 1173 Group variable (i): stfip Number of groups = 51 Obs per group: min = 23 avg = 23.0 max = 23 Wald chi2(25) = 167.61 Log likelihood = -2241.6767 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ homicides | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- abort_rate | -3.310965 .5989897 -5.53 0.000 -4.484963 -2.136967 birthsl | .1731268 .1687956 1.03 0.305 -.1577064 .50396 popdensity | .0019954 .0004799 4.16 0.000 .0010549 .0029359 _Iyear_1979 | -.2625993 .1012527 -2.59 0.010 -.461051 -.0641476 _Iyear_1980 | .0181275 .0949475 0.19 0.849 -.1679663 .2042212 _Iyear_1981 | -.1116823 .0982633 -1.14 0.256 -.3042749 .0809103 _Iyear_1982 | .0554491 .0941289 0.59 0.556 -.1290402 .2399383 _Iyear_1983 | -.0054506 .0954463 -0.06 0.954 -.1925219 .1816207 _Iyear_1984 | -.1537287 .0988907 -1.55 0.120 -.3475509 .0400935 _Iyear_1985 | -.0711666 .0968919 -0.73 0.463 -.2610712 .1187381 _Iyear_1986 | .1849461 .090922 2.03 0.042 .0067421 .36315 _Iyear_1987 | .1184543 .0919553 1.29 0.198 -.0617748 .2986835 _Iyear_1988 | .040212 .0941736 0.43 0.669 -.1443648 .2247888 _Iyear_1989 | .0629612 .0944643 0.67 0.505 -.1221855 .2481079 _Iyear_1990 | .102417 .0947308 1.08 0.280 -.0832519 .2880858 _Iyear_1991 | .2432982 .0916551 2.65 0.008 .0636575 .4229388 _Iyear_1992 | .0262967 .0948865 0.28 0.782 -.1596774 .2122709 _Iyear_1993 | .0843833 .0929496 0.91 0.364 -.0977946 .2665612 _Iyear_1994 | .0154736 .0941172 0.16 0.869 -.1689927 .19994 _Iyear_1995 | -.0289087 .0948213 -0.30 0.760 -.2147551 .1569376 _Iyear_1996 | .0053101 .0939377 0.06 0.955 -.1788044 .1894246 _Iyear_1997 | -.1350449 .100135 -1.35 0.177 -.331306 .0612161 _Iyear_1998 | -.279818 .1047417 -2.67 0.008 -.4851079 -.0745281 _Iyear_1999 | -.308213 .106616 -2.89 0.004 -.5171766 -.0992495 _Iyear_2000 | -.1975787 .099581 -1.98 0.047 -.3927538 -.0024036 ------------------------------------------------------------------------------ . * run a negative binomial fixed effects; . * the conditioning variable is stfip; . xtnbreg homicide `xlist1', i(stfip) fe; Iteration 0: log likelihood = -2229.1839 Iteration 1: log likelihood = -2202.5892 (not concave) Iteration 2: log likelihood = -2197.6073 Iteration 3: log likelihood = -2192.7195 Iteration 4: log likelihood = -2191.9148 Iteration 5: log likelihood = -2191.9116 Iteration 6: log likelihood = -2191.9116 Conditional FE negative binomial regression Number of obs = 1173 Group variable (i): stfip Number of groups = 51 Obs per group: min = 23 avg = 23.0 max = 23 Wald chi2(25) = 95.11 Log likelihood = -2191.9116 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ homicides | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- abort_rate | -2.443432 .6335029 -3.86 0.000 -3.685075 -1.201789 birthsl | .3830904 .1272827 3.01 0.003 .1336209 .63256 popdensity | .0019006 .0004429 4.29 0.000 .0010325 .0027688 _Iyear_1979 | -.3092614 .133153 -2.32 0.020 -.5702363 -.0482864 _Iyear_1980 | .0070456 .1225983 0.06 0.954 -.2332427 .2473339 _Iyear_1981 | -.1878871 .1286954 -1.46 0.144 -.4401255 .0643513 _Iyear_1982 | -.0322388 .1239387 -0.26 0.795 -.2751542 .2106765 _Iyear_1983 | -.0545519 .1241691 -0.44 0.660 -.2979188 .188815 _Iyear_1984 | -.1963953 .1285564 -1.53 0.127 -.4483612 .0555705 _Iyear_1985 | -.1158206 .1255518 -0.92 0.356 -.3618977 .1302565 _Iyear_1986 | .0911199 .1195615 0.76 0.446 -.1432164 .3254561 _Iyear_1987 | .0409474 .1211539 0.34 0.735 -.1965099 .2784047 _Iyear_1988 | -.0191855 .122062 -0.16 0.875 -.2584226 .2200516 _Iyear_1989 | .0120584 .1210542 0.10 0.921 -.2252034 .2493202 _Iyear_1990 | .0048714 .1217371 0.04 0.968 -.233729 .2434718 _Iyear_1991 | .1514406 .1180527 1.28 0.200 -.0799386 .3828197 _Iyear_1992 | -.0751867 .1239565 -0.61 0.544 -.318137 .1677636 _Iyear_1993 | .0584278 .1196823 0.49 0.625 -.1761452 .2930008 _Iyear_1994 | -.0616657 .1236439 -0.50 0.618 -.3040033 .1806719 _Iyear_1995 | -.0669097 .1232543 -0.54 0.587 -.3084837 .1746642 _Iyear_1996 | -.0273683 .122543 -0.22 0.823 -.2675482 .2128117 _Iyear_1997 | -.1511112 .1301783 -1.16 0.246 -.4062561 .1040337 _Iyear_1998 | -.3273883 .1372007 -2.39 0.017 -.5962967 -.0584798 _Iyear_1999 | -.3244441 .1376475 -2.36 0.018 -.5942281 -.05466 _Iyear_2000 | -.1888123 .1274755 -1.48 0.139 -.4386597 .0610351 _cons | -1.828157 1.468619 -1.24 0.213 -4.706598 1.050284 ------------------------------------------------------------------------------ . * compare to a nbreg model with all the state; . * effects added. pick the nbreg model with a ; . * constant dispersion factor. in this case; . * actively add in state fips; . nbreg homicides `xlist2', dispersion(constant); Fitting Poisson model: Iteration 0: log likelihood = -11667.399 Iteration 1: log likelihood = -5351.0885 Iteration 2: log likelihood = -2631.8162 Iteration 3: log likelihood = -2395.6113 Iteration 4: log likelihood = -2392.9729 Iteration 5: log likelihood = -2392.9717 Iteration 6: log likelihood = -2392.9717 Fitting constant-only model: Iteration 0: log likelihood = -3916.4209 Iteration 1: log likelihood = -3187.299 Iteration 2: log likelihood = -3117.6893 Iteration 3: log likelihood = -3117.1805 Iteration 4: log likelihood = -3117.1805 Fitting full model: Iteration 0: log likelihood = -3117.1805 (not concave) Iteration 1: log likelihood = -3069.7512 (not concave) Iteration 2: log likelihood = -3010.958 (not concave) Iteration 3: log likelihood = -2941.7169 Iteration 4: log likelihood = -2702.9966 Iteration 5: log likelihood = -2386.8701 Iteration 6: log likelihood = -2331.9302 Iteration 7: log likelihood = -2325.223 Iteration 8: log likelihood = -2324.8764 Iteration 9: log likelihood = -2324.8744 Iteration 10: log likelihood = -2324.8744 Negative binomial regression Number of obs = 1173 LR chi2(75) = 1584.61 Dispersion = constant Prob > chi2 = 0.0000 Log likelihood = -2324.8744 Pseudo R2 = 0.2542 ------------------------------------------------------------------------------ homicides | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- abort_rate | -3.330721 .7631858 -4.36 0.000 -4.826538 -1.834905 birthsl | .2561931 .2102121 1.22 0.223 -.155815 .6682011 popdensity | .0021541 .0005551 3.88 0.000 .0010662 .0032421 _Iyear_1979 | -.260287 .1262402 -2.06 0.039 -.5077132 -.0128609 _Iyear_1980 | -.0050796 .11979 -0.04 0.966 -.2398636 .2297044 _Iyear_1981 | -.1146028 .1229581 -0.93 0.351 -.3555963 .1263907 _Iyear_1982 | .0165398 .1197417 0.14 0.890 -.2181495 .2512292 _Iyear_1983 | -.047115 .1212133 -0.39 0.698 -.2846887 .1904587 _Iyear_1984 | -.168723 .1242188 -1.36 0.174 -.4121873 .0747413 _Iyear_1985 | -.1011395 .1224561 -0.83 0.409 -.341149 .13887 _Iyear_1986 | .1630293 .1146796 1.42 0.155 -.0617385 .3877972 _Iyear_1987 | .0790579 .1166317 0.68 0.498 -.149536 .3076518 _Iyear_1988 | .0372651 .1176788 0.32 0.751 -.193381 .2679112 _Iyear_1989 | .04304 .1187402 0.36 0.717 -.1896865 .2757665 _Iyear_1990 | .07371 .1192337 0.62 0.536 -.1599839 .3074038 _Iyear_1991 | .2212875 .1152839 1.92 0.055 -.0046649 .4472398 _Iyear_1992 | -.0010087 .11925 -0.01 0.993 -.2347344 .2327169 _Iyear_1993 | .0676668 .116765 0.58 0.562 -.1611883 .2965219 _Iyear_1994 | -.0117821 .1185737 -0.10 0.921 -.2441822 .2206181 _Iyear_1995 | -.0536278 .1193387 -0.45 0.653 -.2875273 .1802717 _Iyear_1996 | -.0264734 .1184267 -0.22 0.823 -.2585854 .2056386 _Iyear_1997 | -.1786224 .1272345 -1.40 0.160 -.4279975 .0707527 _Iyear_1998 | -.3171805 .1326801 -2.39 0.017 -.5772287 -.0571324 _Iyear_1999 | -.3492567 .1365816 -2.56 0.011 -.6169517 -.0815618 _Iyear_2000 | -.2247699 .1250608 -1.80 0.072 -.4698846 .0203447 _Istfip_2 | .0341484 .5644443 0.06 0.952 -1.072142 1.140439 _Istfip_4 | 1.118552 .264921 4.22 0.000 .5993165 1.637788 _Istfip_5 | .7247749 .3039597 2.38 0.017 .1290249 1.320525 _Istfip_6 | 3.052229 .5234147 5.83 0.000 2.026355 4.078103 _Istfip_8 | 1.604368 .2533004 6.33 0.000 1.107908 2.100828 _Istfip_9 | -.4812408 .4426674 -1.09 0.277 -1.348853 .3863714 _Istfip_10 | -.6800681 .5803494 -1.17 0.241 -1.817532 .4573958 _Istfip_11 | -19.65694 5.671766 -3.47 0.001 -30.7734 -8.540485 _Istfip_12 | .4066264 .4034679 1.01 0.314 -.3841561 1.197409 _Istfip_13 | 1.47573 .2761032 5.34 0.000 .9345781 2.016883 _Istfip_15 | 1.580332 .6046201 2.61 0.009 .3952984 2.765365 _Istfip_16 | -.127262 .4437789 -0.29 0.774 -.9970527 .7425287 _Istfip_17 | 1.142358 .346323 3.30 0.001 .4635774 1.821139 _Istfip_18 | .6718917 .268001 2.51 0.012 .1466194 1.197164 _Istfip_19 | .3262424 .3090432 1.06 0.291 -.279471 .9319559 _Istfip_20 | -.1247358 .3796384 -0.33 0.742 -.8688134 .6193417 _Istfip_21 | .6152306 .2739737 2.25 0.025 .078252 1.152209 _Istfip_22 | 1.039275 .2551087 4.07 0.000 .5392712 1.539279 _Istfip_23 | -.3875203 .4757912 -0.81 0.415 -1.320054 .5450132 _Istfip_24 | .5992902 .3373734 1.78 0.076 -.0619495 1.26053 _Istfip_25 | -.2810417 .4636013 -0.61 0.544 -1.189684 .6276002 _Istfip_26 | 1.62512 .3068068 5.30 0.000 1.02379 2.226451 _Istfip_27 | .903204 .2643746 3.42 0.001 .3850393 1.421369 _Istfip_28 | 1.586963 .2717838 5.84 0.000 1.054277 2.11965 _Istfip_29 | 1.529365 .2460435 6.22 0.000 1.047128 2.011601 _Istfip_30 | -.9336686 .6393057 -1.46 0.144 -2.186685 .3193476 _Istfip_31 | .4028805 .3635385 1.11 0.268 -.309642 1.115403 _Istfip_32 | 1.324569 .3658442 3.62 0.000 .6075281 2.041611 _Istfip_33 | -.4914987 .4813869 -1.02 0.307 -1.435 .4520023 _Istfip_34 | .0458734 .5917943 0.08 0.938 -1.114022 1.205769 _Istfip_35 | .6072157 .3534046 1.72 0.086 -.0854446 1.299876 _Istfip_36 | 2.491166 .4513533 5.52 0.000 1.606529 3.375802 _Istfip_37 | 1.252852 .2762841 4.53 0.000 .7113456 1.794359 _Istfip_38 | -1.247988 .6797543 -1.84 0.066 -2.580282 .0843059 _Istfip_39 | 1.389086 .3228155 4.30 0.000 .756379 2.021792 _Istfip_40 | 1.396558 .2618734 5.33 0.000 .8832957 1.909821 _Istfip_41 | 1.055104 .2880809 3.66 0.000 .4904762 1.619733 _Istfip_42 | 1.680037 .3186815 5.27 0.000 1.055433 2.304641 _Istfip_44 | -1.48679 .6891669 -2.16 0.031 -2.837533 -.136048 _Istfip_45 | .9949779 .2601682 3.82 0.000 .4850577 1.504898 _Istfip_46 | -.6859515 .577579 -1.19 0.235 -1.817986 .4460825 _Istfip_47 | .7387778 .2692065 2.74 0.006 .2111427 1.266413 _Istfip_48 | 2.252063 .4143072 5.44 0.000 1.440035 3.06409 _Istfip_49 | .0590502 .3345961 0.18 0.860 -.5967461 .7148465 _Istfip_50 | -.7785908 .6560736 -1.19 0.235 -2.064471 .5072898 _Istfip_51 | 1.612568 .2621281 6.15 0.000 1.098806 2.126329 _Istfip_53 | 1.462439 .2669464 5.48 0.000 .9392334 1.985644 _Istfip_54 | -2.554359 .7754445 -3.29 0.001 -4.074202 -1.034515 _Istfip_55 | 1.16165 .2527121 4.60 0.000 .6663435 1.656957 _Istfip_56 | -.1709723 .5938403 -0.29 0.773 -1.334878 .9929334 _cons | -1.873988 2.360633 -0.79 0.427 -6.500744 2.752767 -------------+---------------------------------------------------------------- /lndelta | -.4524752 .1236953 -.6949135 -.2100368 -------------+---------------------------------------------------------------- delta | .6360519 .0786766 .4991176 .8105544 ------------------------------------------------------------------------------ Likelihood-ratio test of delta=0: chibar2(01) = 136.19 Prob>=chibar2 = 0.000 . * on shortcoming of the conditional fixed effects and; . * negative binomial models is they canot exploit; . * correlation in observations within a cluster. for; . * mle models, within group correlation is estimated using a; . * procedure suggested by liang and zeger; . nbreg homicides `xlist2', dispersion(constant) robust cluster(stfip); Fitting Poisson model: Iteration 0: log pseudolikelihood = -11667.399 Iteration 1: log pseudolikelihood = -5351.0885 Iteration 2: log pseudolikelihood = -2631.8162 Iteration 3: log pseudolikelihood = -2395.6113 Iteration 4: log pseudolikelihood = -2392.9729 Iteration 5: log pseudolikelihood = -2392.9717 Iteration 6: log pseudolikelihood = -2392.9717 Fitting constant-only model: Iteration 0: log pseudolikelihood = -3916.4209 Iteration 1: log pseudolikelihood = -3187.299 Iteration 2: log pseudolikelihood = -3117.6893 Iteration 3: log pseudolikelihood = -3117.1805 Iteration 4: log pseudolikelihood = -3117.1805 Fitting full model: Iteration 0: log pseudolikelihood = -3117.1805 (not concave) Iteration 1: log pseudolikelihood = -3069.7512 (not concave) Iteration 2: log pseudolikelihood = -3010.958 (not concave) Iteration 3: log pseudolikelihood = -2941.7169 Iteration 4: log pseudolikelihood = -2702.9966 Iteration 5: log pseudolikelihood = -2386.8701 Iteration 6: log pseudolikelihood = -2331.9302 Iteration 7: log pseudolikelihood = -2325.223 Iteration 8: log pseudolikelihood = -2324.8764 Iteration 9: log pseudolikelihood = -2324.8744 Iteration 10: log pseudolikelihood = -2324.8744 Negative binomial regression Number of obs = 1173 Dispersion = constant Wald chi2(24) = . Log pseudolikelihood = -2324.8744 Prob > chi2 = . (Std. Err. adjusted for 51 clusters in stfip) ------------------------------------------------------------------------------ | Robust homicides | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- abort_rate | -3.330721 1.003151 -3.32 0.001 -5.296862 -1.364581 birthsl | .2561931 .3699942 0.69 0.489 -.4689822 .9813683 popdensity | .0021541 .0005221 4.13 0.000 .0011309 .0031774 _Iyear_1979 | -.260287 .0995509 -2.61 0.009 -.4554032 -.0651709 _Iyear_1980 | -.0050796 .1061299 -0.05 0.962 -.2130904 .2029313 _Iyear_1981 | -.1146028 .1287274 -0.89 0.373 -.3669039 .1376983 _Iyear_1982 | .0165398 .1389189 0.12 0.905 -.2557363 .288816 _Iyear_1983 | -.047115 .1381362 -0.34 0.733 -.3178571 .2236271 _Iyear_1984 | -.168723 .117421 -1.44 0.151 -.398864 .061418 _Iyear_1985 | -.1011395 .1109916 -0.91 0.362 -.3186791 .1164 _Iyear_1986 | .1630293 .1423923 1.14 0.252 -.1160544 .442113 _Iyear_1987 | .0790579 .1217505 0.65 0.516 -.1595686 .3176845 _Iyear_1988 | .0372651 .1177234 0.32 0.752 -.1934685 .2679987 _Iyear_1989 | .04304 .121391 0.35 0.723 -.194882 .280962 _Iyear_1990 | .07371 .1417423 0.52 0.603 -.2040999 .3515198 _Iyear_1991 | .2212875 .1311944 1.69 0.092 -.0358488 .4784237 _Iyear_1992 | -.0010087 .1249367 -0.01 0.994 -.2458801 .2438626 _Iyear_1993 | .0676668 .1281202 0.53 0.597 -.1834442 .3187779 _Iyear_1994 | -.0117821 .1025366 -0.11 0.909 -.2127501 .189186 _Iyear_1995 | -.0536278 .1320574 -0.41 0.685 -.3124554 .2051999 _Iyear_1996 | -.0264734 .1314514 -0.20 0.840 -.2841135 .2311666 _Iyear_1997 | -.1786224 .1437916 -1.24 0.214 -.4604487 .1032038 _Iyear_1998 | -.3171805 .1490661 -2.13 0.033 -.6093447 -.0250164 _Iyear_1999 | -.3492567 .1696368 -2.06 0.040 -.6817388 -.0167746 _Iyear_2000 | -.2247699 .1302358 -1.73 0.084 -.4800273 .0304875 _Istfip_2 | .0341484 .7662486 0.04 0.964 -1.467671 1.535968 _Istfip_4 | 1.118552 .042589 26.26 0.000 1.035079 1.202025 _Istfip_5 | .7247749 .2524588 2.87 0.004 .2299648 1.219585 _Istfip_6 | 3.052229 .8597928 3.55 0.000 1.367066 4.737391 _Istfip_8 | 1.604368 .0643978 24.91 0.000 1.47815 1.730585 _Istfip_9 | -.4812408 .2736899 -1.76 0.079 -1.017663 .0551815 _Istfip_10 | -.6800681 .5553146 -1.22 0.221 -1.768465 .4083285 _Istfip_11 | -19.65694 4.981056 -3.95 0.000 -29.41963 -9.894252 _Istfip_12 | .4066264 .4931381 0.82 0.410 -.5599064 1.373159 _Istfip_13 | 1.47573 .2344847 6.29 0.000 1.016149 1.935312 _Istfip_15 | 1.580332 .7648037 2.07 0.039 .0813443 3.07932 _Istfip_16 | -.127262 .5393634 -0.24 0.813 -1.184395 .9298708 _Istfip_17 | 1.142358 .4549087 2.51 0.012 .2507533 2.033963 _Istfip_18 | .6718917 .1289503 5.21 0.000 .4191537 .9246298 _Istfip_19 | .3262424 .1926382 1.69 0.090 -.0513216 .7038064 _Istfip_20 | -.1247358 .2108122 -0.59 0.554 -.5379202 .2884486 _Istfip_21 | .6152306 .0969019 6.35 0.000 .4253064 .8051548 _Istfip_22 | 1.039275 .0656735 15.82 0.000 .9105573 1.167993 _Istfip_23 | -.3875203 .5042011 -0.77 0.442 -1.375736 .6006957 _Istfip_24 | .5992902 .2582317 2.32 0.020 .0931653 1.105415 _Istfip_25 | -.2810417 .459809 -0.61 0.541 -1.182251 .6201675 _Istfip_26 | 1.62512 .3586256 4.53 0.000 .9222271 2.328014 _Istfip_27 | .903204 .0125469 71.99 0.000 .8786125 .9277956 _Istfip_28 | 1.586963 .2169132 7.32 0.000 1.161821 2.012105 _Istfip_29 | 1.529365 .0659532 23.19 0.000 1.400099 1.658631 _Istfip_30 | -.9336686 .6494252 -1.44 0.151 -2.206519 .3391814 _Istfip_31 | .4028805 .3710824 1.09 0.278 -.3244277 1.130189 _Istfip_32 | 1.324569 .3908169 3.39 0.001 .5585824 2.090557 _Istfip_33 | -.4914987 .4781109 -1.03 0.304 -1.428579 .4455814 _Istfip_34 | .0458734 .6861628 0.07 0.947 -1.298981 1.390728 _Istfip_35 | .6072157 .3625088 1.68 0.094 -.1032886 1.31772 _Istfip_36 | 2.491166 .7307306 3.41 0.001 1.05896 3.923371 _Istfip_37 | 1.252852 .2262682 5.54 0.000 .809375 1.69633 _Istfip_38 | -1.247988 .7252054 -1.72 0.085 -2.669365 .1733885 _Istfip_39 | 1.389086 .4245548 3.27 0.001 .5569735 2.221198 _Istfip_40 | 1.396558 .146397 9.54 0.000 1.109625 1.683491 _Istfip_41 | 1.055104 .1533345 6.88 0.000 .7545742 1.355635 _Istfip_42 | 1.680037 .4203112 4.00 0.000 .8562424 2.503832 _Istfip_44 | -1.48679 .4587118 -3.24 0.001 -2.385849 -.5877317 _Istfip_45 | .9949779 .0664527 14.97 0.000 .8647331 1.125223 _Istfip_46 | -.6859515 .7420841 -0.92 0.355 -2.14041 .7685065 _Istfip_47 | .7387778 .0727177 10.16 0.000 .5962538 .8813019 _Istfip_48 | 2.252063 .5971855 3.77 0.000 1.0816 3.422525 _Istfip_49 | .0590502 .2642406 0.22 0.823 -.4588519 .5769523 _Istfip_50 | -.7785908 .7344513 -1.06 0.289 -2.218089 .6609074 _Istfip_51 | 1.612568 .1853845 8.70 0.000 1.249221 1.975915 _Istfip_53 | 1.462439 .1177678 12.42 0.000 1.231618 1.693259 _Istfip_54 | -2.554359 .3863728 -6.61 0.000 -3.311636 -1.797082 _Istfip_55 | 1.16165 .0457444 25.39 0.000 1.071993 1.251308 _Istfip_56 | -.1709723 .8669202 -0.20 0.844 -1.870105 1.52816 _cons | -1.873988 4.172222 -0.45 0.653 -10.05139 6.303416 -------------+---------------------------------------------------------------- /lndelta | -.4524752 .3277384 -1.094831 .1898803 -------------+---------------------------------------------------------------- delta | .6360519 .2084586 .3345963 1.209105 ------------------------------------------------------------------------------ . log close; log: c:\bill\econ626\abortion_example.log log type: text closed on: 10 Dec 2006, 09:01:50 -------------------------------------------------------------------------------