----------------------------------------------------------------------------------- log: C:\Documents and Settings\salon\Escritorio\abril7.log log type: text opened on: 8 Apr 2005, 16:31:07 . summ Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- styear | 0 stname | 0 state | 0 stcode | 2000 40.6 22.49651 1 82 year | 2000 1969.5 11.54628 1950 1989 -------------+-------------------------------------------------------- persinc | 1972 9214.945 2895.547 2873.966 19905.64 spend | 1950 951.2788 624.1599 196.3242 8215.613 tax | 1964 451.7443 246.8765 68.9481 2526.353 saletax | 1964 296.964 146.2587 68.9481 996.5841 inctax | 1964 114.572 128.4148 0 876.496 -------------+-------------------------------------------------------- corptax | 1964 40.20827 86.77814 0 2366.408 dem1 | 1901 .6073559 .2303807 .01 1 demmaj1 | 1901 .6401894 .480071 0 1 dis1 | 1901 .2056059 .1493673 0 .5 grant | 1719 309.3926 181.1847 24.42345 992.6741 -------------+-------------------------------------------------------- region | 1920 2.604167 1.035877 1 4 popul | 2000 4.021642 4.293457 .137 29.218 aper5_17 | 2000 .2334816 .0333784 .1509037 .3434296 aper65 | 2000 .0981714 .0238772 .0211 .18 demgov | 1962 .5927625 .491445 0 1 -------------+-------------------------------------------------------- cpi | 2000 .540175 .3181663 .241 1.24 bbudget | 2000 .72 .4491112 0 1 initref | 2000 .54 .4985221 0 1 popul2 | 2000 34.59816 81.37649 .018769 853.6915 persinc2 | 1972 9.33e+07 5.67e+07 8259681 3.96e+08 -------------+-------------------------------------------------------- divgov | 1899 .3391259 .4735373 0 1 . summ spend, detail General expenditures per capita (real 1982-4 dlls) ------------------------------------------------------------- Percentiles Smallest 1% 251.9879 196.3242 5% 301.4865 209.565 10% 365.1724 214.7766 Obs 1950 25% 518.5062 222.215 Sum of Wgt. 1950 50% 927.8279 Mean 951.2788 Largest Std. Dev. 624.1599 75% 1236.232 7173.695 90% 1484.739 7356.112 Variance 389575.6 95% 1695.689 7821.762 Skewness 4.731564 99% 2394.61 8215.613 Kurtosis 45.83321 . gen galto = 1 if spend > 1250 (1479 missing values generated) . replace galto = 0 if spend <= 1250 (1479 real changes made) . summ galto spend Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- galto | 2000 .2605 .4390172 0 1 spend | 1950 951.2788 624.1599 196.3242 8215.613 . replace galto = . if spend ==. (50 real changes made, 50 to missing) . summ galto spend Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- galto | 1950 .2415385 .4281257 0 1 spend | 1950 951.2788 624.1599 196.3242 8215.613 . tis t() is year . iis i() is stcode . desc Contains data from C:\Documents and Settings\salon\Escritorio\panelusa.dta obs: 2,000 vars: 27 25 Feb 2005 14:34 size: 198,000 (81.1% of memory free) ------------------------------------------------------------------------------- storage display value variable name type format label variable label ------------------------------------------------------------------------------- styear str6 %9s Year + stname index stname str14 %14s State name state str2 %9s State abbreviation stcode byte %8.0g State numerical code year int %ty Calendar year persinc float %9.0g Personal income per capita (real 1982-4 dlls) spend float %9.0g General expenditures per capita (real 1982-4 dlls) tax float %9.0g Sales+income+corp taxes per capita (real 1982-4 dlls) saletax float %9.0g Sales taxes per capita (real) inctax float %9.0g Income taxes per capita (real) corptax float %9.0g Corporate income taxes per capita (real) dem1 float %9.0g Democrat share in State House demmaj1 byte %8.0g Dem majority =1 if dem1 share > 50%, 0 o.w. dis1 float %9.0g House closeness =abs(dem1-.5) grant float %9.0g Federal grants per capita (real) region byte %8.0g State region popul float %9.0g population in millions aper5_17 float %9.0g % population age 5 to 17 aper65 float %9.0g % population age 65 or more demgov byte %8.0g =1 if governor is democrat, 0 o.w. cpi float %9.0g Consumer price index, 1982-4=1 bbudget byte %8.0g =1 if deficits cannot carry over, 0 o.w. initref byte %8.0g =1 if initiative or referendum allowed, 0 o.w. popul2 float %9.0g Population squared (in millions) persinc2 float %9.0g Personal income per capita squared (real) divgov byte %9.0g =1 if gov & house are opposite party, 0 o.w galto float %9.0g ------------------------------------------------------------------------------- Sorted by: stcode year Note: dataset has changed since last saved . xi: xtreg galto popul dem* aper* divgov i.year, fe i.year _Iyear_1950-1989 (naturally coded; _Iyear_1950 omitted) matsize too small You have attempted to create a matrix with more than 40 rows or columns or to estimate a model with more than 40 variables plus ancillary parameters. You need to increase matsize using the set matsize command; see help matsize. r(908); . set matsize 800 . xi: xtreg galto popul dem* aper* divgov i.year, fe i.year _Iyear_1950-1989 (naturally coded; _Iyear_1950 omitted) Fixed-effects (within) regression Number of obs = 1885 Group variable (i): stcode Number of groups = 49 R-sq: within = 0.4896 Obs per group: min = 14 between = 0.0769 avg = 38.5 overall = 0.3087 max = 40 F(46,1790) = 37.32 corr(u_i, Xb) = -0.1898 Prob > F = 0.0000 ------------------------------------------------------------------------------ galto | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- popul | -.0138897 .0075943 -1.83 0.068 -.0287843 .001005 dem1 | .4842419 .0830971 5.83 0.000 .3212643 .6472194 demmaj1 | -.0349674 .0262466 -1.33 0.183 -.0864446 .0165097 demgov | .0171022 .0150493 1.14 0.256 -.0124139 .0466184 aper5_17 | .6924326 .591393 1.17 0.242 -.4674606 1.852326 aper65 | .3766812 1.019175 0.37 0.712 -1.622217 2.37558 divgov | -.0211216 .0149499 -1.41 0.158 -.0504427 .0081994 _Iyear_1951 | .0160999 .057234 0.28 0.779 -.0961526 .1283523 _Iyear_1952 | .0147183 .0573844 0.26 0.798 -.0978291 .1272658 _Iyear_1953 | .0316457 .0578298 0.55 0.584 -.0817752 .1450667 _Iyear_1954 | .0294904 .0579809 0.51 0.611 -.0842269 .1432077 _Iyear_1955 | -.0092494 .0581967 -0.16 0.874 -.12339 .1048912 _Iyear_1956 | -.0104996 .0586106 -0.18 0.858 -.125452 .1044529 _Iyear_1957 | -.0103477 .0594486 -0.17 0.862 -.1269438 .1062483 _Iyear_1958 | -.0162373 .060235 -0.27 0.788 -.1343757 .1019011 _Iyear_1959 | -.0561424 .0603244 -0.93 0.352 -.174456 .0621712 _Iyear_1960 | -.0582386 .0619973 -0.94 0.348 -.1798333 .063356 _Iyear_1961 | -.0363244 .0620104 -0.59 0.558 -.1579448 .085296 _Iyear_1962 | -.0377514 .0627813 -0.60 0.548 -.1608837 .0853809 _Iyear_1963 | -.0331422 .0637549 -0.52 0.603 -.158184 .0918996 _Iyear_1964 | -.033963 .0646866 -0.53 0.600 -.1608321 .0929061 _Iyear_1965 | -.0389348 .0649193 -0.60 0.549 -.1662603 .0883907 _Iyear_1966 | -.0382225 .0654012 -0.58 0.559 -.1664932 .0900482 _Iyear_1967 | .034593 .0667351 0.52 0.604 -.0962939 .1654798 _Iyear_1968 | .060459 .0674669 0.90 0.370 -.0718632 .1927812 _Iyear_1969 | .0591525 .0676824 0.87 0.382 -.0735924 .1918974 _Iyear_1970 | .1222878 .0666535 1.83 0.067 -.008439 .2530147 _Iyear_1971 | .1359404 .0660939 2.06 0.040 .0063111 .2655697 _Iyear_1972 | .2024756 .0660017 3.07 0.002 .0730271 .331924 _Iyear_1973 | .2433768 .0640096 3.80 0.000 .1178353 .3689183 _Iyear_1974 | .2308888 .063166 3.66 0.000 .1070019 .3547757 _Iyear_1975 | .314491 .0639237 4.92 0.000 .1891181 .4398639 _Iyear_1976 | .4290601 .0634524 6.76 0.000 .3046115 .5535087 _Iyear_1977 | .4313785 .0640843 6.73 0.000 .3056905 .5570664 _Iyear_1978 | .4793342 .0639034 7.50 0.000 .3540011 .6046674 _Iyear_1979 | .4253286 .0640818 6.64 0.000 .2996457 .5510115 _Iyear_1980 | .4904908 .0652788 7.51 0.000 .3624601 .6185215 _Iyear_1981 | .5150617 .0666958 7.72 0.000 .3842519 .6458716 _Iyear_1982 | .4238425 .0672957 6.30 0.000 .291856 .555829 _Iyear_1983 | .483549 .0682469 7.09 0.000 .349697 .617401 _Iyear_1984 | .564048 .068799 8.20 0.000 .4291131 .6989828 _Iyear_1985 | .5881319 .0695383 8.46 0.000 .4517471 .7245167 _Iyear_1986 | .7395233 .0708227 10.44 0.000 .6006195 .878427 _Iyear_1987 | .7625679 .0717548 10.63 0.000 .621836 .9032999 _Iyear_1988 | .8249271 .0727229 11.34 0.000 .6822964 .9675578 _Iyear_1989 | .7974652 .0740039 10.78 0.000 .6523221 .9426083 _cons | -.4002551 .1767686 -2.26 0.024 -.7469497 -.0535605 -------------+---------------------------------------------------------------- sigma_u | .25819146 sigma_e | .27311215 rho | .47193896 (fraction of variance due to u_i) ------------------------------------------------------------------------------ F test that all u_i=0: F(48, 1790) = 19.76 Prob > F = 0.0000 . predict yhat (option xb assumed; fitted values) (101 missing values generated) . summ yhat Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- yhat | 1899 .2494149 .2866574 -.3171008 1.016791 . help xtprobit . xi: i.state i.state _Istate_1-50 (_Istate_1 for state==AK omitted) unrecognized command: _Istate_ r(199); . summ region Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- region | 1920 2.604167 1.035877 1 4 . egen region2 = group(region) (80 missing values generated) . summ region2 Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- region2 | 1920 2.604167 1.035877 1 4 . xtprobit galto dem* popul* divgov aper* persinc region Fitting comparison model: Iteration 0: log likelihood = -1016.7604 Iteration 1: log likelihood = -638.64695 Iteration 2: log likelihood = -581.9377 Iteration 3: log likelihood = -572.36253 Iteration 4: log likelihood = -571.93391 Iteration 5: log likelihood = -571.93273 Fitting full model: rho = 0.0 log likelihood = -571.93274 rho = 0.1 log likelihood = -453.33265 rho = 0.2 log likelihood = -419.55724 rho = 0.3 log likelihood = -404.29359 rho = 0.4 log likelihood = -396.81371 rho = 0.5 log likelihood = -394.37743 rho = 0.6 log likelihood = -397.75573 Iteration 0: log likelihood = -394.37743 Iteration 1: log likelihood = -393.54662 Iteration 2: log likelihood = -393.50946 Iteration 3: log likelihood = -393.50807 Iteration 4: log likelihood = -393.50806 Random-effects probit regression Number of obs = 1857 Group variable (i): stcode Number of groups = 47 Random effects u_i ~ Gaussian Obs per group: min = 17 avg = 39.5 max = 40 Wald chi2(2) = . Log likelihood = -393.50806 Prob > chi2 = . ------------------------------------------------------------------------------ galto | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- dem1 | .3064675 . . . . . demmaj1 | .2389492 . . . . . demgov | -.0086245 .1339568 -0.06 0.949 -.2711749 .253926 popul | -.400799 . . . . . popul2 | .0180593 . . . . . divgov | -.0654324 .1419123 -0.46 0.645 -.3435754 .2127107 aper5_17 | -3.600544 . . . . . aper65 | 9.117363 . . . . . persinc | .0004553 . . . . . region | .1579857 . . . . . _cons | -5.289284 . . . . . -------------+---------------------------------------------------------------- /lnsig2u | .0018467 .1832403 -.3572977 .3609911 -------------+---------------------------------------------------------------- sigma_u | 1.000924 .0917048 .8363996 1.197811 rho | .5004617 .04581 .4116139 .5892803 ------------------------------------------------------------------------------ Likelihood-ratio test of rho=0: chibar2(01) = 356.85 Prob >= chibar2 = 0.000 . xtprobit galto dem* popul* divgov aper* persinc Fitting comparison model: Iteration 0: log likelihood = -1056.4163 Iteration 1: log likelihood = -650.16723 Iteration 2: log likelihood = -590.46564 Iteration 3: log likelihood = -581.31536 Iteration 4: log likelihood = -580.98116 Iteration 5: log likelihood = -580.98057 Fitting full model: rho = 0.0 log likelihood = -580.98056 rho = 0.1 log likelihood = -461.00327 rho = 0.2 log likelihood = -427.67047 rho = 0.3 log likelihood = -412.8967 rho = 0.4 log likelihood = -404.41104 rho = 0.5 log likelihood = -401.81723 rho = 0.6 log likelihood = -407.90523 Iteration 0: log likelihood = -401.81722 Iteration 1: log likelihood = -320.66227 Iteration 2: log likelihood = -290.10838 Iteration 3: log likelihood = -289.36003 Iteration 4: log likelihood = -279.11883 Iteration 5: log likelihood = -271.84134 Iteration 6: log likelihood = -271.49946 Iteration 7: log likelihood = -271.49766 Random-effects probit regression Number of obs = 1885 Group variable (i): stcode Number of groups = 49 Random effects u_i ~ Gaussian Obs per group: min = 14 avg = 38.5 max = 40 Wald chi2(9) = 230.57 Log likelihood = -271.49766 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ galto | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- dem1 | 3.055247 .8344229 3.66 0.000 1.419808 4.690685 demmaj1 | -.6561513 .2822292 -2.32 0.020 -1.20931 -.1029923 demgov | -.6215541 .1675137 -3.71 0.000 -.9498749 -.2932334 popul | -1.10827 .1112696 -9.96 0.000 -1.326355 -.8901859 popul2 | .0485208 .0049633 9.78 0.000 .0387929 .0582486 divgov | -.2079602 .1739668 -1.20 0.232 -.5489289 .1330085 aper5_17 | -23.21244 4.187261 -5.54 0.000 -31.41932 -15.00556 aper65 | 8.950133 5.466325 1.64 0.102 -1.763668 19.66393 persinc | .0015288 .0001139 13.42 0.000 .0013056 .001752 _cons | -11.28356 1.867873 -6.04 0.000 -14.94453 -7.6226 -------------+---------------------------------------------------------------- /lnsig2u | 1.888901 .1401634 1.614185 2.163616 -------------+---------------------------------------------------------------- sigma_u | 2.5714 .180208 2.241382 2.950008 rho | .8686301 .0159943 .8339917 .8969343 ------------------------------------------------------------------------------ Likelihood-ratio test of rho=0: chibar2(01) = 618.97 Prob >= chibar2 = 0.000 . xtlogit galto dem* popul* divgov aper* persinc, fe note: multiple positive outcomes within groups encountered. note: 12 groups (405 obs) dropped due to all positive or all negative outcomes. Iteration 0: log likelihood = -615.20083 Iteration 1: log likelihood = -271.66121 Iteration 2: log likelihood = -181.7019 Iteration 3: log likelihood = -140.29244 Iteration 4: log likelihood = -123.00686 Iteration 5: log likelihood = -117.66586 Iteration 6: log likelihood = -116.7587 Iteration 7: log likelihood = -116.71308 Iteration 8: log likelihood = -116.71219 Iteration 9: log likelihood = -116.71219 Conditional fixed-effects logistic regression Number of obs = 1480 Group variable (i): stcode Number of groups = 37 Obs per group: min = 40 avg = 40.0 max = 40 LR chi2(9) = 1189.39 Log likelihood = -116.71219 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ galto | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- dem1 | 4.253412 2.437505 1.74 0.081 -.5240095 9.030834 demmaj1 | -1.208344 .695685 -1.74 0.082 -2.571861 .1551736 demgov | -.3648124 .4523218 -0.81 0.420 -1.251347 .5217221 popul | 2.655295 2.423574 1.10 0.273 -2.094823 7.405413 popul2 | -.041664 .0717886 -0.58 0.562 -.1823671 .0990392 divgov | .0815348 .4103226 0.20 0.842 -.7226827 .8857523 aper5_17 | -28.97741 13.18905 -2.20 0.028 -54.82748 -3.127343 aper65 | 105.2799 38.51917 2.73 0.006 29.78373 180.7761 persinc | .0022686 .0003262 6.95 0.000 .0016292 .0029079 ------------------------------------------------------------------------------ . xtlogit galto dem* popul* divgov aper* persinc, re Fitting comparison model: Iteration 0: log likelihood = -1056.4163 Iteration 1: log likelihood = -658.64948 Iteration 2: log likelihood = -596.83409 Iteration 3: log likelihood = -585.43563 Iteration 4: log likelihood = -584.79333 Iteration 5: log likelihood = -584.79055 Fitting full model: tau = 0.0 log likelihood = -584.79055 tau = 0.1 log likelihood = -514.28682 tau = 0.2 log likelihood = -473.40135 tau = 0.3 log likelihood = -446.77531 tau = 0.4 log likelihood = -428.10486 tau = 0.5 log likelihood = -414.8092 tau = 0.6 log likelihood = -405.06718 tau = 0.7 log likelihood = -397.11734 tau = 0.8 log likelihood = -399.98218 Iteration 0: log likelihood = -397.11734 Iteration 1: log likelihood = -329.27973 Iteration 2: log likelihood = -299.33365 Iteration 3: log likelihood = -296.57004 Iteration 4: log likelihood = -294.04242 Iteration 5: log likelihood = -286.64764 Iteration 6: log likelihood = -280.68395 Iteration 7: log likelihood = -280.15209 Iteration 8: log likelihood = -280.14903 Iteration 9: log likelihood = -280.14903 Random-effects logistic regression Number of obs = 1885 Group variable (i): stcode Number of groups = 49 Random effects u_i ~ Gaussian Obs per group: min = 14 avg = 38.5 max = 40 Wald chi2(9) = 173.47 Log likelihood = -280.14903 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ galto | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- dem1 | .2072705 1.387815 0.15 0.881 -2.512797 2.927338 demmaj1 | -.3575132 .5268236 -0.68 0.497 -1.390068 .6750421 demgov | -.2641214 .3090611 -0.85 0.393 -.86987 .3416273 popul | -1.171983 .1357209 -8.64 0.000 -1.437991 -.9059749 popul2 | .0426375 .0063632 6.70 0.000 .0301659 .0551091 divgov | .1734224 .3093011 0.56 0.575 -.4327967 .7796414 aper5_17 | -53.96195 8.999206 -6.00 0.000 -71.60006 -36.32383 aper65 | 2.15688 10.17757 0.21 0.832 -17.79078 22.10454 persinc | .0026386 .0002175 12.13 0.000 .0022122 .0030649 _cons | -13.67322 3.535967 -3.87 0.000 -20.60359 -6.742856 -------------+---------------------------------------------------------------- /lnsig2u | 2.973496 .1603463 2.659223 3.287769 -------------+---------------------------------------------------------------- sigma_u | 4.422689 .354581 3.779574 5.175233 rho | .8560235 .0197622 .8128103 .8906036 ------------------------------------------------------------------------------ Likelihood-ratio test of rho=0: chibar2(01) = 609.28 Prob >= chibar2 = 0.000 . xtprobit galto dem* popul* divgov aper* persinc, nolog Random-effects probit regression Number of obs = 1885 Group variable (i): stcode Number of groups = 49 Random effects u_i ~ Gaussian Obs per group: min = 14 avg = 38.5 max = 40 Wald chi2(9) = 230.57 Log likelihood = -271.49766 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ galto | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- dem1 | 3.055247 .8344229 3.66 0.000 1.419808 4.690685 demmaj1 | -.6561513 .2822292 -2.32 0.020 -1.20931 -.1029923 demgov | -.6215541 .1675137 -3.71 0.000 -.9498749 -.2932334 popul | -1.10827 .1112696 -9.96 0.000 -1.326355 -.8901859 popul2 | .0485208 .0049633 9.78 0.000 .0387929 .0582486 divgov | -.2079602 .1739668 -1.20 0.232 -.5489289 .1330085 aper5_17 | -23.21244 4.187261 -5.54 0.000 -31.41932 -15.00556 aper65 | 8.950133 5.466325 1.64 0.102 -1.763668 19.66393 persinc | .0015288 .0001139 13.42 0.000 .0013056 .001752 _cons | -11.28356 1.867873 -6.04 0.000 -14.94453 -7.6226 -------------+---------------------------------------------------------------- /lnsig2u | 1.888901 .1401634 1.614185 2.163616 -------------+---------------------------------------------------------------- sigma_u | 2.5714 .180208 2.241382 2.950008 rho | .8686301 .0159943 .8339917 .8969343 ------------------------------------------------------------------------------ Likelihood-ratio test of rho=0: chibar2(01) = 618.97 Prob >= chibar2 = 0.000 . help xtlogit --Break-- r(1); . probit galto dem* popul* divgov aper* persinc, nolog Probit estimates Number of obs = 1885 LR chi2(9) = 950.87 Prob > chi2 = 0.0000 Log likelihood = -580.98057 Pseudo R2 = 0.4500 ------------------------------------------------------------------------------ galto | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- dem1 | .3994443 .3861524 1.03 0.301 -.3574005 1.156289 demmaj1 | .4029543 .1509237 2.67 0.008 .1071492 .6987594 demgov | -.007853 .0915376 -0.09 0.932 -.1872635 .1715575 popul | -.4182516 .0355176 -11.78 0.000 -.4878649 -.3486383 popul2 | .0187025 .0019239 9.72 0.000 .0149316 .0224733 divgov | -.0506405 .091803 -0.55 0.581 -.230571 .12929 aper5_17 | -4.726369 1.932857 -2.45 0.014 -8.514698 -.9380391 aper65 | 4.563723 2.429978 1.88 0.060 -.1989466 9.326392 persinc | .0004509 .000028 16.13 0.000 .0003961 .0005057 _cons | -4.118311 .7967233 -5.17 0.000 -5.67986 -2.556762 ------------------------------------------------------------------------------ note: 0 failures and 2 successes completely determined. . logit galto dem* popul* divgov aper* persinc, nolog Logit estimates Number of obs = 1885 LR chi2(9) = 943.25 Prob > chi2 = 0.0000 Log likelihood = -584.79055 Pseudo R2 = 0.4464 ------------------------------------------------------------------------------ galto | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- dem1 | .695837 .6834829 1.02 0.309 -.6437649 2.035439 demmaj1 | .6780589 .2645679 2.56 0.010 .1595154 1.196603 demgov | -.0660391 .1623338 -0.41 0.684 -.3842074 .2521293 popul | -.7372496 .0636187 -11.59 0.000 -.86194 -.6125592 popul2 | .0328207 .0033867 9.69 0.000 .0261828 .0394586 divgov | -.1415451 .1631836 -0.87 0.386 -.461379 .1782888 aper5_17 | -8.394936 3.402974 -2.47 0.014 -15.06464 -1.72523 aper65 | 8.631257 4.197858 2.06 0.040 .4036065 16.85891 persinc | .0008054 .0000524 15.38 0.000 .0007028 .0009081 _cons | -7.285481 1.396132 -5.22 0.000 -10.02185 -4.549113 ------------------------------------------------------------------------------ . help mfx . mfx compute , at(mean) Marginal effects after logit y = Pr(galto) (predict) = .110061 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- dem1 | .0681555 .06709 1.02 0.310 -.06333 .199641 .60681 demmaj1*| .0622138 .02287 2.72 0.007 .017394 .107034 .638196 demgov*| -.0065003 .01605 -0.40 0.686 -.037963 .024962 .594164 popul | -.0722118 .00693 -10.42 0.000 -.0858 -.058624 4.18049 popul2 | .0032147 .00036 8.81 0.000 .0025 .00393 36.5095 divgov*| -.0136239 .01554 -0.88 0.381 -.044076 .016828 .338992 aper5_17 | -.8222637 .34705 -2.37 0.018 -1.50248 -.14205 .232768 aper65 | .8454107 .40019 2.11 0.035 .061051 1.62977 .099222 persinc | .0000789 .00001 15.01 0.000 .000069 .000089 9199.01 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 . probit galto dem* popul* divgov aper* persinc, nolog Probit estimates Number of obs = 1885 LR chi2(9) = 950.87 Prob > chi2 = 0.0000 Log likelihood = -580.98057 Pseudo R2 = 0.4500 ------------------------------------------------------------------------------ galto | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- dem1 | .3994443 .3861524 1.03 0.301 -.3574005 1.156289 demmaj1 | .4029543 .1509237 2.67 0.008 .1071492 .6987594 demgov | -.007853 .0915376 -0.09 0.932 -.1872635 .1715575 popul | -.4182516 .0355176 -11.78 0.000 -.4878649 -.3486383 popul2 | .0187025 .0019239 9.72 0.000 .0149316 .0224733 divgov | -.0506405 .091803 -0.55 0.581 -.230571 .12929 aper5_17 | -4.726369 1.932857 -2.45 0.014 -8.514698 -.9380391 aper65 | 4.563723 2.429978 1.88 0.060 -.1989466 9.326392 persinc | .0004509 .000028 16.13 0.000 .0003961 .0005057 _cons | -4.118311 .7967233 -5.17 0.000 -5.67986 -2.556762 ------------------------------------------------------------------------------ note: 0 failures and 2 successes completely determined. . mfx compute , at(mean) Marginal effects after probit y = Pr(galto) (predict) = .11398394 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- dem1 | .0770449 .07467 1.03 0.302 -.069297 .223387 .60681 demmaj1*| .0728487 .02548 2.86 0.004 .022907 .12279 .638196 demgov*| -.001516 .01769 -0.09 0.932 -.036181 .033149 .594164 popul | -.0806724 .0074 -10.91 0.000 -.095172 -.066173 4.18049 popul2 | .0036073 .0004 9.03 0.000 .002824 .00439 36.5095 divgov*| -.0096722 .01743 -0.55 0.579 -.04383 .024486 .338992 aper5_17 | -.9116226 .38778 -2.35 0.019 -1.67166 -.15159 .232768 aper65 | .8802514 .45837 1.92 0.055 -.018128 1.77863 .099222 persinc | .000087 .00001 16.74 0.000 .000077 .000097 9199.01 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 . summ dem* Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- dem1 | 1901 .6073559 .2303807 .01 1 demmaj1 | 1901 .6401894 .480071 0 1 demgov | 1962 .5927625 .491445 0 1 . mfx compute , at(median) Marginal effects after probit y = Pr(galto) (predict) = .14877707 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- dem1 | .0926264 .08432 1.10 0.272 -.072634 .257887 .6 demmaj1*| .0744986 .0272 2.74 0.006 .021178 .127819 1 demgov*| -.0018285 .02134 -0.09 0.932 -.043657 .04 1 popul | -.0969876 .01125 -8.62 0.000 -.119035 -.07494 2.904 popul2 | .0043369 .00054 8.03 0.000 .003278 .005396 8.43322 divgov*| -.0114338 .02059 -0.56 0.579 -.051792 .028925 0 aper5_17 | -1.095989 .45678 -2.40 0.016 -1.99126 -.200713 .2364 aper65 | 1.058273 .56292 1.88 0.060 -.045024 2.16157 .0989 persinc | .0001046 .00001 10.38 0.000 .000085 .000124 9236.06 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 . help logit . mfx compute , at(median) Marginal effects after probit y = Pr(galto) (predict) = .14877707 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- dem1 | .0926264 .08432 1.10 0.272 -.072634 .257887 .6 demmaj1*| .0744986 .0272 2.74 0.006 .021178 .127819 1 demgov*| -.0018285 .02134 -0.09 0.932 -.043657 .04 1 popul | -.0969876 .01125 -8.62 0.000 -.119035 -.07494 2.904 popul2 | .0043369 .00054 8.03 0.000 .003278 .005396 8.43322 divgov*| -.0114338 .02059 -0.56 0.579 -.051792 .028925 0 aper5_17 | -1.095989 .45678 -2.40 0.016 -1.99126 -.200713 .2364 aper65 | 1.058273 .56292 1.88 0.060 -.045024 2.16157 .0989 persinc | .0001046 .00001 10.38 0.000 .000085 .000124 9236.06 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 . . summ galto Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- galto | 1950 .2415385 .4281257 0 1 . help mfx . help clarify . . . help xtlogit . help clogit . help xtcloglog . log close log: C:\Documents and Settings\salon\Escritorio\abril7.log log type: text closed on: 8 Apr 2005, 17:18:20 ---------------------------------------------------------------------------------