log type: text
opened on: 24 Oct 2005, 11:06:36
. use "C:\AAA Miker Files\newer web pages\soc_388_notes\Qian 80-90 intermar.dta", clear
. * This is Qian's dataset
. describe
Contains data from C:\AAA Miker Files\newer web pages\soc_388_notes\Qian 80-90 intermar.dta
obs: 512
vars: 6 16 Oct 2001 11:12
size: 10,752 (99.9% of memory free)
-------------------------------------------------------------------------------
storage display value
variable name type format label variable label
-------------------------------------------------------------------------------
mfulleth str5 %9s
med4 byte %8.0g
ffulleth str5 %9s
fed4 byte %8.0g
count long %12.0g COUNT
year byte %8.0g
-------------------------------------------------------------------------------
Sorted by: year med4 fed4
. tabulate mfulleth [fweight=count]
mfulleth | Freq. Percent Cum.
------------+-----------------------------------
Asian | 742 0.14 0.14
Hisp | 22,925 4.38 4.52
black | 36,429 6.96 11.48
white | 463,446 88.52 100.00
------------+-----------------------------------
Total | 523,542 100.00
. tabulate med4 [fweight=count]
med4 | Freq. Percent Cum.
------------+-----------------------------------
1 | 74,785 14.28 14.28
2 | 218,475 41.73 56.01
3 | 135,645 25.91 81.92
4 | 94,637 18.08 100.00
------------+-----------------------------------
Total | 523,542 100.00
. display 4*4*4*4*2
512
. *The first model is the everything is independent from everything model
. desmat: poisson count mfulleth med4 ffulleth fed4 year
------------------------------------------------------------------------------------------
Poisson regression
------------------------------------------------------------------------------------------
Dependent variable count
Optimization: ml
Number of observations: 512
Initial log likelihood: -1402408.286
Log likelihood: -289165.150
LR chi square: 2226486.273
Model degrees of freedom: 13
Pseudo R-squared: 0.794
Prob: 0.000
------------------------------------------------------------------------------------------
nr Effect Coeff s.e.
------------------------------------------------------------------------------------------
count
mfulleth
1 Hisp 3.431** 0.037
2 black 3.894** 0.037
3 white 6.437** 0.037
med4
4 2 1.072** 0.004
5 3 0.595** 0.005
6 4 0.235** 0.005
ffulleth
7 Hisp 3.262** 0.035
8 black 3.695** 0.034
9 white 6.281** 0.034
fed4
10 2 1.229** 0.004
11 3 0.733** 0.005
12 4 0.142** 0.005
year
13 90 -0.415** 0.003
14 _cons -4.279** 0.050
------------------------------------------------------------------------------------------
* p < .05
** p < .01
. set linesize 79
. poisgof
Goodness-of-fit chi2 = 576133.3
Prob > chi2(498) = 0.0000
. *How about some of the relevant second order interactions
. desmat: poisson count mfulleth*med4 ffulleth*fed4 med4*year fed4*year
-------------------------------------------------------------------------------
Poisson regression
-------------------------------------------------------------------------------
Dependent variable count
Optimization: ml
Number of observations: 512
Initial log likelihood: -1402408.286
Log likelihood: -273859.008
LR chi square: 2257098.556
Model degrees of freedom: 37
Pseudo R-squared: 0.805
Prob: 0.000
-------------------------------------------------------------------------------
nr Effect Coeff s.e.
-------------------------------------------------------------------------------
count
mfulleth
1 Hisp 5.740** 0.219
2 black 5.804** 0.219
3 white 7.978** 0.218
med4
4 2 1.871** 0.235
5 3 2.314** 0.227
6 4 2.790** 0.225
mfulleth.med4
7 Hisp.2 -1.506** 0.235
8 Hisp.3 -2.624** 0.228
9 Hisp.4 -4.150** 0.226
10 black.2 -0.972** 0.235
11 black.3 -2.149** 0.228
12 black.4 -3.553** 0.226
13 white.2 -0.701** 0.235
14 white.3 -1.786** 0.227
15 white.4 -2.413** 0.225
ffulleth
16 Hisp 5.193** 0.172
17 black 5.137** 0.172
18 white 7.415** 0.172
fed4
19 2 1.740** 0.186
20 3 1.839** 0.182
21 4 2.242** 0.179
ffulleth.fed4
22 Hisp.2 -1.235** 0.187
23 Hisp.3 -2.235** 0.183
24 Hisp.4 -3.838** 0.182
25 black.2 -0.708** 0.187
26 black.3 -1.550** 0.182
27 black.4 -2.896** 0.180
28 white.2 -0.400* 0.186
29 white.3 -1.297** 0.182
30 white.4 -2.123** 0.179
year
31 90 -0.666** 0.011
med4.year
32 2.90 -0.067** 0.009
33 3.90 0.346** 0.009
34 4.90 0.001 0.010
fed4.year
35 2.90 -0.103** 0.009
36 3.90 0.620** 0.010
37 4.90 0.365** 0.011
38 _cons -7.009** 0.278
-------------------------------------------------------------------------------
* p < .05
** p < .01
. poisgof
Goodness-of-fit chi2 = 545521
Prob > chi2(474) = 0.0000
. *There are 10 possible combinations of 2nd order interactions if you have 5 variables, 5*4/2, divided by 2 because we don't care about the order
. * Here we were looking at 4 of those 10 possible combinations
. *Desmat builds the interactions hierarchically by default, that is it includes all lower order interactions automatically unless you tell it otherwise.
. *Second of all, repetitive terms are dropped- we had two interactions with year, but the direct effects of year were only included once.
. desmat: poisson count mfulleth*med4 ffulleth*fed4 med4*year fed4*year, verbose
Desmat generated the following design matrix:
nr Variables Term Parameterization
First Last
1 _x_1 _x_3 mfulleth ind(1)
2 _x_4 _x_6 med4 ind(1)
3 _x_7 _x_15 mfulleth.med4 ind(1).ind(1)
4 _x_16 _x_18 ffulleth ind(1)
5 _x_19 _x_21 fed4 ind(1)
6 _x_22 _x_30 ffulleth.fed4 ind(1).ind(1)
7 _x_31 year ind(80)
8 _x_32 _x_34 med4.year ind(1).ind(80)
9 _x_35 _x_37 fed4.year ind(1).ind(80)
Iteration 0: log likelihood = -1105130
Iteration 1: log likelihood = -539645.18
Iteration 2: log likelihood = -304737.23
Iteration 3: log likelihood = -274369.14
Iteration 4: log likelihood = -273879.28
Iteration 5: log likelihood = -273859.67
Iteration 6: log likelihood = -273859.01
Iteration 7: log likelihood = -273859.01
Poisson regression Number of obs = 512
LR chi2(37) = 2257098.56
Prob > chi2 = 0.0000
Log likelihood = -273859.01 Pseudo R2 = 0.8047
------------------------------------------------------------------------------
count | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_x_1 | 5.739566 .2185604 26.26 0.000 5.311195 6.167936
_x_2 | 5.804343 .2185384 26.56 0.000 5.376016 6.232671
_x_3 | 7.9784 .2182472 36.56 0.000 7.550643 8.406157
_x_4 | 1.870895 .2348303 7.97 0.000 1.410636 2.331154
_x_5 | 2.31376 .227366 10.18 0.000 1.868131 2.75939
_x_6 | 2.789941 .2248429 12.41 0.000 2.349257 3.230625
_x_7 | -1.505677 .2353645 -6.40 0.000 -1.966983 -1.044371
_x_8 | -2.624426 .2280659 -11.51 0.000 -3.071427 -2.177425
_x_9 | -4.150211 .2264714 -18.33 0.000 -4.594087 -3.706335
_x_10 | -.9720078 .2352398 -4.13 0.000 -1.433069 -.5109463
_x_11 | -2.148525 .2278797 -9.43 0.000 -2.595161 -1.701889
_x_12 | -3.552744 .2258113 -15.73 0.000 -3.995326 -3.110162
_x_13 | -.7010277 .2348528 -2.98 0.003 -1.161331 -.2407247
_x_14 | -1.785697 .2273878 -7.85 0.000 -2.231369 -1.340025
_x_15 | -2.412682 .2248711 -10.73 0.000 -2.853421 -1.971942
_x_16 | 5.192956 .1719743 30.20 0.000 4.855893 5.53002
_x_17 | 5.136835 .1720017 29.87 0.000 4.799718 5.473952
_x_18 | 7.415298 .1715501 43.23 0.000 7.079066 7.751531
_x_19 | 1.740136 .1864657 9.33 0.000 1.37467 2.105602
_x_20 | 1.839102 .1816897 10.12 0.000 1.482997 2.195208
_x_21 | 2.242404 .1792553 12.51 0.000 1.89107 2.593738
_x_22 | -1.235357 .1871495 -6.60 0.000 -1.602163 -.8685506
_x_23 | -2.234654 .1826073 -12.24 0.000 -2.592558 -1.87675
_x_24 | -3.838236 .1815907 -21.14 0.000 -4.194148 -3.482325
_x_25 | -.7079302 .1870721 -3.78 0.000 -1.074585 -.3412757
_x_26 | -1.54954 .1823952 -8.50 0.000 -1.907028 -1.192052
_x_27 | -2.895551 .1804844 -16.04 0.000 -3.249294 -2.541808
_x_28 | -.4003496 .1864989 -2.15 0.032 -.7658807 -.0348184
_x_29 | -1.296759 .1817184 -7.14 0.000 -1.652921 -.9405977
_x_30 | -2.122822 .1792924 -11.84 0.000 -2.474229 -1.771416
_x_31 | -.6655906 .0106087 -62.74 0.000 -.6863833 -.6447979
_x_32 | -.0671178 .0087363 -7.68 0.000 -.0842405 -.049995
_x_33 | .3463791 .0092862 37.30 0.000 .3281785 .3645796
_x_34 | .0007133 .010067 0.07 0.944 -.0190177 .0204443
_x_35 | -.1033578 .0091227 -11.33 0.000 -.121238 -.0854777
_x_36 | .6204118 .0095909 64.69 0.000 .601614 .6392096
_x_37 | .3645222 .0107398 33.94 0.000 .3434725 .385572
_cons | -7.009354 .2775615 -25.25 0.000 -7.553365 -6.465343
------------------------------------------------------------------------------
-------------------------------------------------------------------------------
Poisson regression
-------------------------------------------------------------------------------
Dependent variable count
Optimization: ml
Number of observations: 512
Initial log likelihood: -1402408.286
Log likelihood: -273859.008
LR chi square: 2257098.556
Model degrees of freedom: 37
Pseudo R-squared: 0.805
Prob: 0.000
-------------------------------------------------------------------------------
nr Effect Coeff s.e.
-------------------------------------------------------------------------------
count
mfulleth
1 Hisp 5.740** 0.219
2 black 5.804** 0.219
3 white 7.978** 0.218
med4
4 2 1.871** 0.235
5 3 2.314** 0.227
6 4 2.790** 0.225
mfulleth.med4
7 Hisp.2 -1.506** 0.235
8 Hisp.3 -2.624** 0.228
9 Hisp.4 -4.150** 0.226
10 black.2 -0.972** 0.235
11 black.3 -2.149** 0.228
12 black.4 -3.553** 0.226
13 white.2 -0.701** 0.235
14 white.3 -1.786** 0.227
15 white.4 -2.413** 0.225
ffulleth
16 Hisp 5.193** 0.172
17 black 5.137** 0.172
18 white 7.415** 0.172
fed4
19 2 1.740** 0.186
20 3 1.839** 0.182
21 4 2.242** 0.179
ffulleth.fed4
22 Hisp.2 -1.235** 0.187
23 Hisp.3 -2.235** 0.183
24 Hisp.4 -3.838** 0.182
25 black.2 -0.708** 0.187
26 black.3 -1.550** 0.182
27 black.4 -2.896** 0.180
28 white.2 -0.400* 0.186
29 white.3 -1.297** 0.182
30 white.4 -2.123** 0.179
year
31 90 -0.666** 0.011
med4.year
32 2.90 -0.067** 0.009
33 3.90 0.346** 0.009
34 4.90 0.001 0.010
fed4.year
35 2.90 -0.103** 0.009
36 3.90 0.620** 0.010
37 4.90 0.365** 0.011
38 _cons -7.009** 0.278
-------------------------------------------------------------------------------
* p < .05
** p < .01
. *You nearly always want to build these models hierarchically, but if for some reason you absolutely want to make a second order interaction without the lower order terms being included, you replace the asterisk with the period in desmat
. desmat: poisson count mfulleth*med4 ffulleth*fed4 med4.year fed4.year, verbose
Desmat generated the following design matrix:
nr Variables Term Parameterization
First Last
1 _x_1 _x_3 mfulleth ind(1)
2 _x_4 _x_6 med4 ind(1)
3 _x_7 _x_15 mfulleth.med4 ind(1).ind(1)
4 _x_16 _x_18 ffulleth ind(1)
5 _x_19 _x_21 fed4 ind(1)
6 _x_22 _x_30 ffulleth.fed4 ind(1).ind(1)
7 _x_31 _x_33 med4.year ind(1).ind(80)
8 _x_34 _x_36 fed4.year ind(1).ind(80)
Iteration 0: log likelihood = -1109177.2
Iteration 1: log likelihood = -541904.76
Iteration 2: log likelihood = -305926.71
Iteration 3: log likelihood = -276338.13
Iteration 4: log likelihood = -275895.07
Iteration 5: log likelihood = -275884.43
Iteration 6: log likelihood = -275884.32
Iteration 7: log likelihood = -275884.32
Poisson regression Number of obs = 512
LR chi2(36) = 2253047.93
Prob > chi2 = 0.0000
Log likelihood = -275884.32 Pseudo R2 = 0.8033
------------------------------------------------------------------------------
count | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_x_1 | 5.739638 .2185683 26.26 0.000 5.311252 6.168024
_x_2 | 5.804416 .2185464 26.56 0.000 5.376073 6.232759
_x_3 | 7.978473 .2182551 36.56 0.000 7.550701 8.406245
_x_4 | 2.01404 .2348321 8.58 0.000 1.553778 2.474302
_x_5 | 2.456906 .2273679 10.81 0.000 2.011273 2.902538
_x_6 | 2.933087 .2248448 13.04 0.000 2.492399 3.373774
_x_7 | -1.50575 .2353719 -6.40 0.000 -1.96707 -1.044429
_x_8 | -2.624498 .2280735 -11.51 0.000 -3.071514 -2.177482
_x_9 | -4.150284 .226479 -18.33 0.000 -4.594175 -3.706393
_x_10 | -.9720805 .2352471 -4.13 0.000 -1.433156 -.5110045
_x_11 | -2.148598 .2278872 -9.43 0.000 -2.595248 -1.701947
_x_12 | -3.552817 .2258189 -15.73 0.000 -3.995414 -3.11022
_x_13 | -.7011004 .2348602 -2.99 0.003 -1.161418 -.2407829
_x_14 | -1.78577 .2273954 -7.85 0.000 -2.231456 -1.340083
_x_15 | -2.412754 .2248788 -10.73 0.000 -2.853509 -1.972
_x_16 | 5.192957 .1719743 30.20 0.000 4.855893 5.53002
_x_17 | 5.136836 .1720017 29.87 0.000 4.799719 5.473953
_x_18 | 7.415299 .1715502 43.23 0.000 7.079067 7.751531
_x_19 | 1.890149 .1864579 10.14 0.000 1.524698 2.255599
_x_20 | 1.989115 .1816817 10.95 0.000 1.633025 2.345204
_x_21 | 2.392416 .1792472 13.35 0.000 2.041098 2.743734
_x_22 | -1.235357 .1871495 -6.60 0.000 -1.602163 -.8685508
_x_23 | -2.234654 .1826073 -12.24 0.000 -2.592558 -1.876751
_x_24 | -3.838237 .1815908 -21.14 0.000 -4.194148 -3.482325
_x_25 | -.7079305 .1870721 -3.78 0.000 -1.074585 -.341276
_x_26 | -1.54954 .1823952 -8.50 0.000 -1.907028 -1.192053
_x_27 | -2.895551 .1804845 -16.04 0.000 -3.249294 -2.541808
_x_28 | -.4003499 .1864989 -2.15 0.032 -.7658811 -.0348186
_x_29 | -1.29676 .1817184 -7.14 0.000 -1.652921 -.940598
_x_30 | -2.122823 .1792924 -11.84 0.000 -2.474229 -1.771416
_x_31 | -.4051205 .0067792 -59.76 0.000 -.4184076 -.3918335
_x_32 | .0083763 .0074745 1.12 0.262 -.0062735 .0230261
_x_33 | -.3372895 .008425 -40.03 0.000 -.3538022 -.3207768
_x_34 | -.4797963 .0067201 -71.40 0.000 -.4929675 -.4666252
_x_35 | .2439733 .0073431 33.22 0.000 .229581 .2583655
_x_36 | -.0119163 .0087909 -1.36 0.175 -.0291462 .0053136
_cons | -7.282791 .2775407 -26.24 0.000 -7.826761 -6.738821
------------------------------------------------------------------------------
-------------------------------------------------------------------------------
Poisson regression
-------------------------------------------------------------------------------
Dependent variable count
Optimization: ml
Number of observations: 512
Initial log likelihood: -1402408.286
Log likelihood: -275884.320
LR chi square: 2253047.934
Model degrees of freedom: 36
Pseudo R-squared: 0.803
Prob: 0.000
-------------------------------------------------------------------------------
nr Effect Coeff s.e.
-------------------------------------------------------------------------------
count
mfulleth
1 Hisp 5.740** 0.219
2 black 5.804** 0.219
3 white 7.978** 0.218
med4
4 2 2.014** 0.235
5 3 2.457** 0.227
6 4 2.933** 0.225
mfulleth.med4
7 Hisp.2 -1.506** 0.235
8 Hisp.3 -2.624** 0.228
9 Hisp.4 -4.150** 0.226
10 black.2 -0.972** 0.235
11 black.3 -2.149** 0.228
12 black.4 -3.553** 0.226
13 white.2 -0.701** 0.235
14 white.3 -1.786** 0.227
15 white.4 -2.413** 0.225
ffulleth
16 Hisp 5.193** 0.172
17 black 5.137** 0.172
18 white 7.415** 0.172
fed4
19 2 1.890** 0.186
20 3 1.989** 0.182
21 4 2.392** 0.179
ffulleth.fed4
22 Hisp.2 -1.235** 0.187
23 Hisp.3 -2.235** 0.183
24 Hisp.4 -3.838** 0.182
25 black.2 -0.708** 0.187
26 black.3 -1.550** 0.182
27 black.4 -2.896** 0.180
28 white.2 -0.400* 0.186
29 white.3 -1.297** 0.182
30 white.4 -2.123** 0.179
med4.year
31 2.90 -0.405** 0.007
32 3.90 0.008 0.007
33 4.90 -0.337** 0.008
fed4.year
34 2.90 -0.480** 0.007
35 3.90 0.244** 0.007
36 4.90 -0.012 0.009
37 _cons -7.283** 0.278
-------------------------------------------------------------------------------
* p < .05
** p < .01
. *We lost one degree of freedom for the direct effects of year.
. poisgof
Goodness-of-fit chi2 = 549571.6
Prob > chi2(475) = 0.0000
. tabulate mfulleth
mfulleth | Freq. Percent Cum.
------------+-----------------------------------
Asian | 128 25.00 25.00
Hisp | 128 25.00 50.00
black | 128 25.00 75.00
white | 128 25.00 100.00
------------+-----------------------------------
Total | 512 100.00
. tabulate mfulleth, nolab
mfulleth | Freq. Percent Cum.
------------+-----------------------------------
Asian | 128 25.00 25.00
Hisp | 128 25.00 50.00
black | 128 25.00 75.00
white | 128 25.00 100.00
------------+-----------------------------------
Total | 512 100.00
. gen r_endog=0
. replace r_endog=1 if mfulleth=="Asian" & ffulleth=="Asian"
(32 real changes made)
. replace r_endog=2 if mfulleth=="Hisp" & ffulleth=="Hisp"
(32 real changes made)
. replace r_endog=3 if mfulleth=="black" & ffulleth=="black"
(32 real changes made)
. replace r_endog=4 if mfulleth=="white" & ffulleth=="white"
(32 real changes made)
. desmat: poisson count mfulleth*med4 ffulleth*fed4 year r_endog
-------------------------------------------------------------------------------
Poisson regression
-------------------------------------------------------------------------------
Dependent variable count
Optimization: ml
Number of observations: 512
Initial log likelihood: -1402408.286
Log likelihood: -123146.804
LR chi square: 2558522.965
Model degrees of freedom: 35
Pseudo R-squared: 0.912
Prob: 0.000
-------------------------------------------------------------------------------
nr Effect Coeff s.e.
-------------------------------------------------------------------------------
count
mfulleth
1 Hisp 5.526** 0.222
2 black 3.649** 0.223
3 white 6.035** 0.224
med4
4 2 1.846** 0.235
5 3 2.461** 0.227
6 4 2.790** 0.225
mfulleth.med4
7 Hisp.2 -1.506** 0.235
8 Hisp.3 -2.624** 0.228
9 Hisp.4 -4.150** 0.226
10 black.2 -0.972** 0.235
11 black.3 -2.149** 0.228
12 black.4 -3.553** 0.226
13 white.2 -0.701** 0.235
14 white.3 -1.786** 0.227
15 white.4 -2.413** 0.225
ffulleth
16 Hisp 4.831** 0.175
17 black 1.810** 0.178
18 white 5.377** 0.177
fed4
19 2 1.705** 0.186
20 3 2.105** 0.182
21 4 2.387** 0.179
ffulleth.fed4
22 Hisp.2 -1.235** 0.187
23 Hisp.3 -2.235** 0.183
24 Hisp.4 -3.838** 0.182
25 black.2 -0.708** 0.187
26 black.3 -1.550** 0.182
27 black.4 -2.896** 0.180
28 white.2 -0.400* 0.186
29 white.3 -1.297** 0.182
30 white.4 -2.123** 0.179
year
31 90 -0.415** 0.003
r_endog
32 1 4.497** 0.089
33 2 2.107** 0.042
34 3 6.914** 0.050
35 4 2.871** 0.042
36 _cons -5.892** 0.282
-------------------------------------------------------------------------------
* p < .05
** p < .01
. poisgof
Goodness-of-fit chi2 = 244096.6
Prob > chi2(476) = 0.0000
. desmat: poisson count mfulleth*med4*fed4 ffulleth*fed4*med4 year r_endog
-------------------------------------------------------------------------------
Poisson regression
-------------------------------------------------------------------------------
Dependent variable count
Optimization: ml
Number of observations: 512
Initial log likelihood: -1402408.286
Log likelihood: -10672.055
LR chi square: 2783472.463
Model degrees of freedom: 116
Pseudo R-squared: 0.992
Prob: 0.000
-------------------------------------------------------------------------------
nr Effect Coeff s.e.
-------------------------------------------------------------------------------
count
mfulleth
1 Hisp 5.374** 0.382
2 black 3.227** 0.388
3 white 5.893** 0.383
med4
4 2 0.639 0.566
5 3 0.651 0.620
6 4 -1.406 1.305
mfulleth.med4
7 Hisp.2 -1.193* 0.470
8 Hisp.3 -1.907** 0.521
9 Hisp.4 -2.073 1.135
10 black.2 -0.650 0.479
11 black.3 -1.098* 0.538
12 black.4 -2.042 1.203
13 white.2 -0.818 0.469
14 white.3 -1.637** 0.519
15 white.4 -1.278 1.125
fed4
16 2 0.343 0.584
17 3 -1.335 1.119
18 4 -0.738 1.116
mfulleth.fed4
19 Hisp.2 -0.881 0.482
20 Hisp.3 0.524 1.080
21 Hisp.4 -1.202 1.149
22 black.2 -0.600 0.491
23 black.3 0.987 1.088
24 black.4 -0.922 1.205
25 white.2 -0.568 0.481
26 white.3 0.754 1.079
27 white.4 -0.811 1.141
med4.fed4
28 2.2 1.901** 0.699
29 2.3 3.225** 1.189
30 2.4 0.520 1.283
31 3.2 1.712* 0.749
32 3.3 4.484** 1.207
33 3.4 3.269** 1.208
34 4.2 2.680 1.394
35 4.3 6.282** 1.667
36 4.4 6.928** 1.661
mfulleth.med4.fed4
37 Hisp.2.2 0.506 0.567
38 Hisp.2.3 -1.314 1.127
39 Hisp.2.4 0.755 1.294
40 Hisp.3.2 0.613 0.616
41 Hisp.3.3 -1.314 1.141
42 Hisp.3.4 0.316 1.215
43 Hisp.4.2 -0.458 1.191
44 Hisp.4.3 -1.875 1.525
45 Hisp.4.4 -0.390 1.572
46 black.2.2 0.361 0.579
47 black.2.3 -1.549 1.138
48 black.2.4 0.944 1.351
49 black.3.2 0.335 0.635
50 black.3.3 -1.734 1.155
51 black.3.4 0.284 1.275
52 black.4.2 0.189 1.264
53 black.4.3 -1.352 1.583
54 black.4.4 0.055 1.662
55 white.2.2 0.625 0.566
56 white.2.3 -1.074 1.125
57 white.2.4 1.284 1.284
58 white.3.2 0.746 0.613
59 white.3.3 -1.024 1.139
60 white.3.4 0.648 1.205
61 white.4.2 -0.325 1.180
62 white.4.3 -1.472 1.516
63 white.4.4 -0.034 1.558
ffulleth
64 Hisp 4.401** 0.271
65 black 1.628** 0.280
66 white 5.213** 0.271
ffulleth.fed4
67 Hisp.2 -0.016 0.409
68 Hisp.3 -1.071* 0.470
69 Hisp.4 -2.292** 0.695
70 black.2 0.278 0.419
71 black.3 -0.660 0.488
72 black.4 -1.463 0.781
73 white.2 0.330 0.408
74 white.3 -0.742 0.468
75 white.4 -1.903** 0.679
ffulleth.med4
76 Hisp.2 -0.039 0.401
77 Hisp.3 -0.558 0.485
78 Hisp.4 -0.780 1.101
79 black.2 -0.173 0.411
80 black.3 -1.091* 0.503
81 black.4 -0.072 1.173
82 white.2 0.115 0.400
83 white.3 -0.493 0.482
84 white.4 -0.721 1.092
ffulleth.fed4.med4
85 Hisp.2.2 -1.019* 0.514
86 Hisp.2.3 -0.710 0.591
87 Hisp.2.4 -0.028 1.186
88 Hisp.3.2 -0.410 0.574
89 Hisp.3.3 -0.003 0.627
90 Hisp.3.4 -0.313 1.174
91 Hisp.4.2 0.143 0.817
92 Hisp.4.3 0.140 0.816
93 Hisp.4.4 -0.076 1.276
94 black.2.2 -0.871 0.528
95 black.2.3 -0.507 0.612
96 black.2.4 -1.177 1.260
97 black.3.2 -0.213 0.595
98 black.3.3 0.253 0.653
99 black.3.4 -1.412 1.249
100 black.4.2 0.357 0.903
101 black.4.3 0.253 0.902
102 black.4.4 -0.946 1.385
103 white.2.2 -0.989 0.513
104 white.2.3 -0.536 0.588
105 white.2.4 0.279 1.175
106 white.3.2 -0.348 0.570
107 white.3.3 0.108 0.623
108 white.3.4 -0.019 1.164
109 white.4.2 0.519 0.799
110 white.4.3 0.588 0.800
111 white.4.4 0.625 1.259
year
112 90 -0.415** 0.003
r_endog
113 1 4.139** 0.095
114 2 2.003** 0.043
115 3 6.921** 0.050
116 4 2.878** 0.042
117 _cons -4.370** 0.438
-------------------------------------------------------------------------------
* p < .05
** p < .01
. poisgof
Goodness-of-fit chi2 = 19147.11
Prob > chi2(395) = 0.0000
* See my excel file.