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  • Observations completely determined in teffects, but running okay with logit

    Hi. When running teffects I get the error message that "514 observations completely determined; the model, as specified, is not identified", whereas when I run the same in a the first stage logit of psmatch, it appears to run fine. Could someone please explain to me what is happening here?

    Code:
    . teffects ipwra (rate_con_avg7day rate_test_avg7day) (stepone pop_perkm2_2019 p
    > overtyperct deathrate_2019 infantmortrate_2019 birthrate_2019 naturalgrwthrate
    > _2019 rate_hospital_total1k rate_hospital_beds_total1k exphealth_percap1516 ra
    > te_denguecase2018 rate_typoidtot_case2018 rate_acuteresp_totcase2018 rate_pneu
    > moniatot_case2018 rate_tb2018 rate_influenzacase_2018 rate_hepcases_2018 rate_
    > htnanddm2018 rate_diabetes2018 rate_hypertension2018, logit), iterate(100)
    treatment model has 514 observations completely determined; the model, as
    specified, is not identified
    
    
    . logit stepone pop_perkm2_2019 povertyperct deathrate_2019 infantmortrate_2019 
    > birthrate_2019 naturalgrwthrate_2019 rate_hospital_total1k rate_hospital_beds_
    > total1k exphealth_percap1516 rate_denguecase2018 rate_typoidtot_case2018 rate_
    > acuteresp_totcase2018 rate_pneumoniatot_case2018 rate_tb2018 rate_influenzacas
    > e_2018 rate_hepcases_2018 rate_htnanddm2018 rate_diabetes2018 rate_hypertensio
    > n2018, iterate(100)
    
    Iteration 0:  Log likelihood =   -1018.14  
    Iteration 1:  Log likelihood = -576.63862  
    Iteration 2:  Log likelihood = -497.33311  
    Iteration 3:  Log likelihood = -458.15125  
    Iteration 4:  Log likelihood = -419.39634  
    Iteration 5:  Log likelihood = -412.38457  
    Iteration 6:  Log likelihood = -380.40379  
    Iteration 7:  Log likelihood = -373.20497  
    Iteration 8:  Log likelihood =   -371.238  
    Iteration 9:  Log likelihood = -368.93039  
    Iteration 10: Log likelihood =  -366.7781  
    Iteration 11: Log likelihood = -366.70591  
    Iteration 12: Log likelihood = -366.70366  
    Iteration 13: Log likelihood = -366.70365  
    
    Logistic regression                                    Number of obs =   1,579
                                                           LR chi2(19)   = 1302.87
                                                           Prob > chi2   =  0.0000
    Log likelihood = -366.70365                            Pseudo R2     =  0.6398
    
    -------------------------------------------------------------------------------
          stepone | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
    --------------+----------------------------------------------------------------
    pop_perk~2019 |   .0069034    .000922     7.49   0.000     .0050962    .0087105
     povertyperct |   4.126038   .4687399     8.80   0.000     3.207325    5.044752
    deathrat~2019 |   188.9415   23.98367     7.88   0.000     141.9344    235.9487
    infantmo~2019 |   .7319597   .1323898     5.53   0.000     .4724805    .9914388
    birthrat~2019 |  -181.3161   23.26441    -7.79   0.000    -226.9135   -135.7187
    naturalg~2019 |   172.2302   22.24448     7.74   0.000     128.6318    215.8286
    rat~l_total1k |  -18.78166   2.067758    -9.08   0.000    -22.83439   -14.72893
    rate_hospit.. |    2.72845   .3054729     8.93   0.000     2.129734    3.327166
    exphea~ap1516 |  -.0090102   .0009747    -9.24   0.000    -.0109206   -.0070998
    rate_de~e2018 |   11.19207   3.770649     2.97   0.003     3.801733    18.58241
    rate_typoid.. |  -.0172644   .2757447    -0.06   0.950    -.5577141    .5231853
    rat~tcase2018 |   .0001276   .0061045     0.02   0.983    -.0118369    .0120921
    rate_pneumo.. |  -17.54092    1.96893    -8.91   0.000    -21.39995   -13.68189
      rate_tb2018 |   8.007212   .8000535    10.01   0.000     6.439136    9.575289
    rate_i~e_2018 |  -173.7376   28.44892    -6.11   0.000    -229.4965   -117.9788
    rate_hepcas~8 |   45.07249   5.162552     8.73   0.000     34.95407     55.1909
    rate_htn~2018 |   -1022715   161381.8    -6.34   0.000     -1339017   -706412.2
    rate_dia~2018 |    2001747   237605.1     8.42   0.000      1536049     2467444
    rate_hyp~2018 |  -124296.8   32170.22    -3.86   0.000    -187349.3   -61244.35
            _cons |  -94.38065    11.0117    -8.57   0.000    -115.9632   -72.79811
    -------------------------------------------------------------------------------
    Note: 514 failures and 0 successes completely determined.

  • #2
    Logit also shows 514 zero outcomes perfectly predicted. That means 1/phat is not defined for those observations, and so you can’t use ipw or ipwra without some adjustment.

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