Help interpreting etregress results

[France Maliwat]

Good day, everyone! I am a beginner at Stata and I am conducting my undergraduate thesis. I came across the etregress command in one of my reference material.

I would just like to ask how to interpret the results. All I have been able to gather is that if /athrho is insignificant, we fail to reject the null hypothesis that there is no endogeniety, and even then I am not sure if that is correct.

I also read in this forum something about lambda and the Mills ratio. I would also like to ask how to interpret lambda and how to apply it in the discussion of my results.

Lastly, I would also like to know how to interpret the last line of the output in Stata:
Wald test of indep. eqns. (rho = 0): chi2(1) = 0.44 Prob > chi2 = 0.5082

I am sorry for asking such beginner questions. I hope you can guide me to better understanding. Thank you for your patience.

. etregress lnmpcccons i.gender age agesq ib1.education ib1.occupation i.spousework hhsize i.urbanrural i.agrihh ib0.region, treat( savings= i.gender ib1.education ib1.occupation
>  i.urbanrural ib0.majorregion)

Iteration 0:   log likelihood = -47519.925  
Iteration 1:   log likelihood = -47516.814  
Iteration 2:   log likelihood = -47516.674  
Iteration 3:   log likelihood = -47516.674  

Linear regression with endogenous treatment       Number of obs   =      40171
Estimator: maximum likelihood                     Wald chi2(35)   =   64028.51
Log likelihood = -47516.674                       Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
lnmpcccons   |
      gender |
     Female  |   .0200998   .0069047     2.91   0.004      .006567    .0336327
         age |   .0221425   .0011321    19.56   0.000     .0199236    .0243614
       agesq |  -.0001795   .0000108   -16.62   0.000    -.0002007   -.0001584
             |
   education |
     lessHS  |  -.2481496   .0070105   -35.40   0.000      -.26189   -.2344092
     postHS  |   .1825727    .015147    12.05   0.000     .1528852    .2122603
   undrColl  |   .1883856    .010537    17.88   0.000     .1677334    .2090378
   gradColl  |   .5408572   .0124758    43.35   0.000      .516405    .5653094
             |
  occupation |
       None  |  -.0942983   .0089295   -10.56   0.000    -.1117997   -.0767969
       Srvc  |  -.1813907    .012531   -14.48   0.000    -.2059511   -.1568303
        FFF  |   -.231482   .0097222   -23.81   0.000    -.2505371   -.2124268
      Trade  |  -.2085894   .0113729   -18.34   0.000    -.2308798   -.1862991
      Machn  |  -.1758853   .0128373   -13.70   0.000     -.201046   -.1507246
  Unskilled  |  -.2556389   .0095443   -26.78   0.000    -.2743454   -.2369324
    Special  |  -.2060041   .0443966    -4.64   0.000    -.2930198   -.1189885
             |
  spousework |
   Employed  |   .0603052   .0054892    10.99   0.000     .0495465    .0710639
      hhsize |  -.1173599   .0011544  -101.66   0.000    -.1196224   -.1150973
             |
  urbanrural |
      Rural  |  -.2207572   .0067701   -32.61   0.000    -.2340263   -.2074881
             |
      agrihh |
       Agri  |  -.2653004   .0074923   -35.41   0.000     -.279985   -.2506158
             |
      region |
        CAR  |  -.3130079   .0153421   -20.40   0.000    -.3430778    -.282938
    RegionI  |  -.2882502   .0144554   -19.94   0.000    -.3165823   -.2599181
   RegionII  |  -.2827657   .0150802   -18.75   0.000    -.3123224    -.253209
  RegionIII  |  -.0449132   .0126781    -3.54   0.000    -.0697618   -.0200645
  RegionIVA  |  -.1205495   .0117696   -10.24   0.000    -.1436174   -.0974815
  RegionIVB  |  -.4996433   .0160114   -31.21   0.000    -.5310251   -.4682616
    RegionV  |  -.4008348   .0144166   -27.80   0.000    -.4290909   -.3725787
   RegionVI  |  -.3842323   .0139014   -27.64   0.000    -.4114786    -.356986
  RegionVII  |  -.4257214   .0141991   -29.98   0.000    -.4535511   -.3978917
 RegionVIII  |  -.5009164   .0152565   -32.83   0.000    -.5308185   -.4710143
   RegionIX  |  -.6382387   .0158506   -40.27   0.000    -.6693054   -.6071721
    RegionX  |   -.571888   .0156281   -36.59   0.000    -.6025185   -.5412576
   RegionXI  |  -.3430152   .0145741   -23.54   0.000    -.3715798   -.3144505
  RegionXII  |   -.467563   .0150714   -31.02   0.000    -.4971025   -.4380235
       ARMM  |  -.2962025   .0162721   -18.20   0.000    -.3280953   -.2643097
     CARAGA  |  -.4357856   .0161733   -26.94   0.000    -.4674846   -.4040866
             |
     savings |   .3587962   .0324674    11.05   0.000     .2951613    .4224312
       _cons |   8.287544   .0331734   249.83   0.000     8.222525    8.352563
-------------+----------------------------------------------------------------
savings      |
      gender |
     Female  |   -.042895   .0191203    -2.24   0.025      -.08037     -.00542
             |
   education |
     lessHS  |  -.2711064   .0191815   -14.13   0.000    -.3087015   -.2335113
     postHS  |   .3252731   .0384256     8.47   0.000     .2499604    .4005859
   undrColl  |   .1818193   .0278539     6.53   0.000     .1272267     .236412
   gradColl  |   .6602118   .0253974    26.00   0.000     .6104338    .7099899
             |
  occupation |
       None  |   -.151249   .0227643    -6.64   0.000    -.1958662   -.1066318
       Srvc  |  -.0633397   .0338512    -1.87   0.061    -.1296868    .0030075
        FFF  |  -.3788235   .0252428   -15.01   0.000    -.4282984   -.3293486
      Trade  |  -.2375267   .0315493    -7.53   0.000    -.2993621   -.1756913
      Machn  |  -.0886138   .0348941    -2.54   0.011     -.157005   -.0202226
  Unskilled  |  -.4453145   .0255374   -17.44   0.000    -.4953669   -.3952621
    Special  |   .1595485   .1181011     1.35   0.177    -.0719254    .3910224
             |
  urbanrural |
      Rural  |  -.2537893   .0176925   -14.34   0.000     -.288466   -.2191126
             |
 majorregion |
  Bal Luzon  |   -.197786   .0253054    -7.82   0.000    -.2473836   -.1481884
    Visayas  |  -.4764414   .0295201   -16.14   0.000    -.5342998    -.418583
   Mindanao  |   -.518607   .0268327   -19.33   0.000    -.5711982   -.4660158
             |
       _cons |  -.1183562   .0271676    -4.36   0.000    -.1716038   -.0651087
-------------+----------------------------------------------------------------
     /athrho |   .0241147   .0364425     0.66   0.508    -.0473112    .0955407
    /lnsigma |  -.6917304   .0035504  -194.83   0.000     -.698689   -.6847718
-------------+----------------------------------------------------------------
         rho |   .0241101   .0364213                     -.0472759     .095251
       sigma |   .5007089   .0017777                      .4972368    .5042053
      lambda |   .0120721   .0182413                     -.0236802    .0478244
------------------------------------------------------------------------------
Wald test of indep. eqns. (rho = 0): chi2(1) =     0.44   Prob > chi2 = 0.5082

[Phil Bromiley]

No one responded quickly to your question. You'll increase your chances of a helpful answer if you follow the FAQ on asking questions - provide Stata code in code delimiters, Stata output, and sample data. Most of the folks on this list do not open files. Also, try to simplify your code to what is necessary to demonstrate the problem. Surely, all these variables are not essential to the problem.

You need to re-read the documentation - it covers many of these issues (although understanding it may take some effort).

The Wald test suggests you don't need etregress but rather can go with one of the estimators that assumes selection on the observables - a very nice thing.