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