------------------------------------------------------------------------------- log: I:\Web\LearnEconometrics\data\stata\mnl.smcl log type: smcl opened on: 7 Apr 2008, 12:41:53 . tab insure insure | Freq. Percent Cum. ------------+----------------------------------- Indemnity | 294 47.73 47.73 Prepaid | 277 44.97 92.69 Uninsure | 45 7.31 100.00 ------------+----------------------------------- Total | 616 100.00 . summarize Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- patid | 644 592838.1 315023.2 3292 997539 noinsur0 | 338 .0710059 .2572155 0 1 noinsur1 | 339 .0707965 .2568637 0 1 noinsur2 | 336 .0535714 .2255058 0 1 age | 643 44.41415 14.22441 18.11087 86.07254 -------------+-------------------------------------------------------- male | 644 .2593168 .4386004 0 1 site2 | 644 .3664596 .4822116 0 1 site3 | 644 .310559 .4630822 0 1 ppd0 | 644 .4751553 .4997705 0 1 ppd1 | 644 .4736025 .4996908 0 1 -------------+-------------------------------------------------------- ppd2 | 616 .4545455 .4983343 0 1 nonwhite | 644 .1956522 .3970103 0 1 ppd | 644 .4736025 .4996908 0 1 insure | 616 1.595779 .6225427 1 3 . mlogit insure age male nonwhite site2 site3, base(2) Iteration 0: log likelihood = -555.85446 Iteration 1: log likelihood = -534.72983 Iteration 2: log likelihood = -534.36536 Iteration 3: log likelihood = -534.36165 Iteration 4: log likelihood = -534.36165 Multinomial logistic regression Number of obs = 615 LR chi2(10) = 42.99 Prob > chi2 = 0.0000 Log likelihood = -534.36165 Pseudo R2 = 0.0387 ------------------------------------------------------------------------------ insure | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- Indemnity | age | .011745 .0061946 1.90 0.058 -.0003962 .0238862 male | -.5616934 .2027465 -2.77 0.006 -.9590693 -.1643175 nonwhite | -.9747768 .2363213 -4.12 0.000 -1.437958 -.5115955 site2 | -.1130359 .2101903 -0.54 0.591 -.5250013 .2989296 site3 | .5879879 .2279351 2.58 0.010 .1412433 1.034733 _cons | -.2697127 .3284422 -0.82 0.412 -.9134476 .3740222 -------------+---------------------------------------------------------------- Uninsure | age | .0039489 .0115994 0.34 0.734 -.0187855 .0266832 male | -.1098438 .3651883 -0.30 0.764 -.8255998 .6059122 nonwhite | -.7577178 .4195759 -1.81 0.071 -1.580071 .0646357 site2 | -1.324599 .4697954 -2.82 0.005 -2.245381 -.4038165 site3 | .3801756 .3728188 1.02 0.308 -.3505358 1.110887 _cons | -1.556656 .5963286 -2.61 0.009 -2.725438 -.387873 ------------------------------------------------------------------------------ (insure==Prepaid is the base outcome) . mfx, predict(p outcome(1)) Marginal effects after mlogit y = Pr(insure==1) (predict, p outcome(1)) = .48179251 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- age | .0028073 .00148 1.90 0.058 -.000096 .005711 44.4683 male*| -.1347111 .04683 -2.88 0.004 -.226494 -.042929 .250407 nonwhite*| -.2138472 .05074 -4.21 0.000 -.313297 -.114397 .196748 site2*| .0096603 .05082 0.19 0.849 -.089942 .109263 .370732 site3*| .1333108 .05294 2.52 0.012 .029558 .237064 .313821 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 . mfx, predict(p outcome(2)) Marginal effects after mlogit y = Pr(insure==2) (predict, p outcome(2)) = .45249028 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- age | -.0026779 .00149 -1.80 0.072 -.005594 .000238 44.4683 male*| .1245862 .04792 2.60 0.009 .030674 .218498 .250407 nonwhite*| .2319489 .05255 4.41 0.000 .12895 .334948 .196748 site2*| .0607169 .05047 1.20 0.229 -.038202 .159636 .370732 site3*| -.1370701 .05195 -2.64 0.008 -.238899 -.035241 .313821 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 . mfx, predict(p outcome(3)) Marginal effects after mlogit y = Pr(insure==3) (predict, p outcome(3)) = .06571721 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- age | -.0001294 .00068 -0.19 0.849 -.001464 .001205 44.4683 male*| .0101249 .02301 0.44 0.660 -.034972 .055222 .250407 nonwhite*| -.0181017 .02071 -0.87 0.382 -.058688 .022485 .196748 site2*| -.0703772 .02151 -3.27 0.001 -.112535 -.028219 .370732 site3*| .0037593 .02182 0.17 0.863 -.039012 .046531 .313821 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 . predict p1 if e(sample), outcome(1) (option pr assumed; predicted probability) (29 missing values generated) . summarize p1 Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- p1 | 615 .4764228 .1032279 .1698142 .71939 . predict idx1, outcome(Indemnity) xb (1 missing value generated) . summarize idx1 Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- idx1 | 643 .0566113 .4962973 -1.700719 1.298198 . list idx1 in 1/20 +-----------+ | idx1 | |-----------| 1. | .4831167 | 2. | -.055111 | 3. | .1712106 | 4. | -.3788345 | 5. | .0925817 | |-----------| 6. | -.034113 | 7. | .0808127 | 8. | -.5177885 | 9. | .0846304 | 10. | .5505552 | |-----------| 11. | .6638318 | 12. | -.7727699 | 13. | .4337645 | 14. | -.7197444 | 15. | -.2503389 | |-----------| 16. | -.180303 | 17. | .1885105 | 18. | -.660481 | 19. | .3021499 | 20. | .7719001 | +-----------+ . log close log: I:\Web\LearnEconometrics\data\stata\mnl.smcl log type: smcl closed on: 7 Apr 2008, 12:45:17 -------------------------------------------------------------------------------