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  • Multinominal logit model, IIA

    Dear all,
    I tray to analyze the determinants of loan repayment performance in a given organization.
    The outcome variable was classified into three categories namely ‘paid on time’ for the clients who repaid loan before the due date, ‘delinquency’ for clients who repaid late from the due date or repaid less than the appropriate amount of their most recent loan, and ‘default’ for the clients who did not pay after three months of the due date. After I run the model using mlogit command i found the following result. But I have some question regarding the model and the iia test.

    1. Is the overall estimation result good as per the title ?
    2. What is the rationality behind choosing base category for specific model?
    3. iia test doesnt work for this model what is the problem with it?

    I need your help.


    Code:
     mlogit loanstatus age loansize income area rooms floor tenur hhsize sex educ
    
    Iteration 0:   log likelihood =  -145.5761  
    Iteration 1:   log likelihood = -75.804603  
    Iteration 2:   log likelihood = -72.239623  
    Iteration 3:   log likelihood = -64.135462  
    Iteration 4:   log likelihood = -62.519257  
    Iteration 5:   log likelihood = -62.061845  
    Iteration 6:   log likelihood = -62.058576  
    Iteration 7:   log likelihood = -62.058576  
    
    Multinomial logistic regression                   Number of obs   =        155
                                                      LR chi2(20)     =     167.04
                                                      Prob > chi2     =     0.0000
    Log likelihood = -62.058576                       Pseudo R2       =     0.5737
    
    ------------------------------------------------------------------------------
      loanstatus |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
    -------------+----------------------------------------------------------------
    Default      |
             age |   .0900269   .0497059     1.81   0.070    -.0073949    .1874487
        loansize |   .0000514   .0000259     1.99   0.047     7.49e-07    .0001021
          income |  -.0014927   .0004514    -3.31   0.001    -.0023774   -.0006079
            area |   .0167898   .0949439     0.18   0.860    -.1692968    .2028765
           rooms |  -3.228844   1.858627    -1.74   0.082    -6.871686    .4139975
           floor |   .0920056   .2192906     0.42   0.675    -.3377962    .5218073
           tenur |  -.0978784   .3479336    -0.28   0.778    -.7798156    .5840589
          hhsize |  -.1732183   .3867932    -0.45   0.654     -.931319    .5848824
             sex |   -.199167   .9524633    -0.21   0.834    -2.065961    1.667627
            educ |  -1.596048   .6462633    -2.47   0.014    -2.862701   -.3293956
           _cons |   .5467191   8.059603     0.07   0.946    -15.24981    16.34325
    -------------+----------------------------------------------------------------
    Delinquent   |
             age |   .0536494   .0354863     1.51   0.131    -.0159024    .1232012
        loansize |   .0000102   .0000203     0.50   0.615    -.0000296    .0000501
          income |  -.0002777   .0001202    -2.31   0.021    -.0005133   -.0000422
            area |     .06586   .0851608     0.77   0.439    -.1010522    .2327721
           rooms |  -1.079745   1.246765    -0.87   0.386     -3.52336    1.363869
           floor |   .3715418   .1467483     2.53   0.011     .0839205    .6591631
           tenur |  -.1872231   .2649991    -0.71   0.480    -.7066118    .3321656
          hhsize |   .0925946   .2187679     0.42   0.672    -.3361827    .5213719
             sex |    .473651   .6225617     0.76   0.447    -.7465475     1.69385
            educ |  -.8049165   .4456839    -1.81   0.071    -1.678441    .0686079
           _cons |   -2.80576   5.615922    -0.50   0.617    -13.81276    8.201244
    -------------+----------------------------------------------------------------
    Paid_on_time |  (base outcome)
    ------------------------------------------------------------------------------
    
    
    . mlogtest, iia
    
    Problem determining number of categories.
    
    **** Hausman tests of IIA assumption
    
     Ho: Odds(Outcome-J vs Outcome-K) are independent of other alternatives.
    You used the old syntax of hausman. Click here to learn about the new syntax.
    
    (storing estimation results as _HAUSMAN)
    flat region resulting in a missing likelihood
    r(430);
    Thank you very much.
    Ermiyas


    Last edited by Ermiyas Gebrie; 19 Dec 2019, 08:34.

  • #2
    You have accidentally posted your topic in Statalist's Mata Forum, which is used for discussions of Stata's Mata language. Your question will see a much larger audience if you post it in Statalist's General Forum.

    Comment


    • #3
      Thank you William Lisowski I already posted in Statalist's General Forum.

      Comment

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