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st: Treatreg output interpretation


From   "Lapitan, Aileen (IRRI)" <a.lapitan@cgiar.org>
To   "'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu>
Subject   st: Treatreg output interpretation
Date   Wed, 29 Sep 2004 06:38:48 -0700

hi,

i've run treatreg using stata as well as a 2-step model to determine impact
of the choice variable of technology adoption (phatpot2b or pot2) on the
income per hectare (profitha). With the second step, this is what i got:

 Source |       SS       df       MS              Number of obs =     587
-------------+------------------------------           F( 21,   565) =
15.54
       Model |  367.309377    21  17.4909227           Prob > F      =
0.0000
    Residual |  635.851023   565  1.12540004           R-squared     =
0.3662
-------------+------------------------------           Adj R-squared =
0.3426
       Total |   1003.1604   586  1.71187782           Root MSE      =
1.0608

----------------------------------------------------------------------------
--
   lprofitha |      Coef.   Std. Err.      t    P>|t|     [95% Conf.
Interval]
-------------+--------------------------------------------------------------
--
       larea |  -.3422196   .0641317    -5.34   0.000    -.4681853
-.2162539
          r9 |   .7074985   .2717812     2.60   0.009     .1736737
1.241323
         r11 |    .449468   .2905934     1.55   0.122    -.1213073
1.020243
      lyield |   .6401999   .0559619    11.44   0.000     .5302811
.7501187
     tenure1 |   .3332755   .1328914     2.51   0.012     .0722541
.5942969
     tenure2 |   .5958305   .1804391     3.30   0.001     .2414171
.9502439
       proof |   .2982886   .1485377     2.01   0.045     .0065351
.5900421
      yield1 |   1.240719   .5314508     2.33   0.020     .1968581
2.284579
      arbdum |  -.1812507    .107392    -1.69   0.092     -.392187
.0296856
    agehdum2 |   .2887208   .1028717     2.81   0.005     .0866632
.4907784
   phatpot2b |  -.2493435   .1585794    -1.57   0.116    -.5608205
.0621336
     coopmem |    .226677   .1021316     2.22   0.027      .026073
.4272809
      labdum |   -.228383   .1124028    -2.03   0.043    -.4491614
-.0076046
          r0 |   .9007858    .280013     3.22   0.001     .3507923
1.450779
          r1 |   .8929879   .1844589     4.84   0.000     .5306789
1.255297
          r2 |   .6005806   .1391465     4.32   0.000     .3272731
.8738881
          r3 |   .6606746   .1849266     3.57   0.000     .2974469
1.023902
          r4 |   .7185069   .2064196     3.48   0.001     .3130634
1.12395
          r5 |   .5458338   .1690473     3.23   0.001     .2137958
.8778717
          r7 |  -.4041432    .226489    -1.78   0.075    -.8490064
.0407201
          r8 |   .4669252   .1860029     2.51   0.012     .1015836
.8322668
       _cons |   7.555088   .2484353    30.41   0.000     7.067118
8.043057


The adjusted R2 is not that high.  Does this mean i won't be able to use
this model to interpret the relationship of the choice variable to the
dependent variable, profitha?


Second question is related to this.Since the fit of the profit equation is
not that large judging by the value of the adjusted R2, does this mean I
can't proceed to analyzing the covariance matrix between the error terms in
the first and second steps? here's the treatreg output:


Treatment effects model -- MLE                  Number of obs      =
221

                                                Wald chi2(9)       =
57.10
Log likelihood = -2404.7144                     Prob > chi2        =
0.0000

----------------------------------------------------------------------------
--
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf.
Interval]
-------------+--------------------------------------------------------------
--
profitha     |
        area |  -979.9762    563.985    -1.74   0.082    -2085.367
125.4141
       yield |   1878.773   412.2671     4.56   0.000     1070.744
2686.802
      yield1 |  -4060.235   4292.129    -0.95   0.344    -12472.65
4352.184
      arcdum |   1985.837   1865.546     1.06   0.287    -1670.566
5642.239
     agehdum |   3572.093   2728.362     1.31   0.190    -1775.397
8919.584
     distdum |   1531.586   1521.883     1.01   0.314     -1451.25
4514.422
        extn |   2109.938   2019.068     1.05   0.296    -1847.364
6067.239
     labdum2 |   14748.99   4642.952     3.18   0.001     5648.969
23849.01
        pot2 |  -11077.05   3577.151    -3.10   0.002    -18088.14
-4065.96
       _cons |   7995.068   3541.648     2.26   0.024     1053.565
14936.57
-------------+--------------------------------------------------------------
--
pot2         |
        area |   .1083771   .1352427     0.80   0.423    -.1566937
.3734479
       irrig |   2.206678   .5429927     4.06   0.000     1.142432
3.270924
    educhdum |  -1.380165   .3418587    -4.04   0.000    -2.050196
-.7101345
        dist |  -.6386896   .2883079    -2.22   0.027    -1.203763
-.0736164
      arcdum |   .2979616    .566744     0.53   0.599    -.8128362
1.408759
      arbdum |    .252918   .3448144     0.73   0.463    -.4229058
.9287417
        ageh |   .0102057   .0140028     0.73   0.466    -.0172393
.0376507
        extn |   .4007376   .3768061     1.06   0.288    -.3377888
1.139264
      credit |   .4109917    .367625     1.12   0.264      -.30954
1.131523
     tenure2 |  -.5366819    .421654    -1.27   0.203    -1.363108
.2897447
       _cons |   .1249916   .9564531     0.13   0.896    -1.749622
1.999605
-------------+--------------------------------------------------------------
--
     /athrho |     .56256   .2833076     1.99   0.047     .0072873
1.117833
    /lnsigma |   9.315738   .0499618   186.46   0.000     9.217814
9.413661
-------------+--------------------------------------------------------------
--
         rho |   .5098743   .2096556                      .0072872
.8068137
       sigma |   11111.52   555.1518                      10075.02
12254.65
      lambda |   5665.479   2433.118                      896.6554
10434.3
----------------------------------------------------------------------------
--
LR test of indep. eqns. (rho = 0):   chi2(1) =     4.16   Prob > chi2 =
0.0414
----------------------------------------------------------------------------
--

hope you can enlighten me on this:) thanks a lot,

aileen
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