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st: RE: re: problem using predictnl after obtaining non-linear estimates


From   Lopa Chakraborti <lchakraborti@arec.umd.edu>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   st: RE: re: problem using predictnl after obtaining non-linear estimates
Date   Sat, 25 Oct 2008 17:40:27 -0400

The underlying model is downstream water quality is a function of upstream quality and BOD (pollutant) discharges of plants in a stream segment. I have included monitoring station level dummy variables as controls and annual dummies for trends. The coefficients on each of the BOD terms are functions of K1 and K2 (the main parameters I am interested in) as well as distances between the plants and distance to the downstream monitor etc. basically these distances do not vary for a given pair of monitoring station and plant(s). Since I am really interested not in K1 and K2 but in the impact of each of the BOD terms given the distances, I tried to use predictnl using the distance data (for bod1 as seen below). Please let me know if this provides enough context of what I am trying to do.
many thanks for any help on this
Lopa
program nlwqfinalnoseasonfinal2
  1. if "`1'"=="?" {
  2.  global S_1 "K1 K2 K3 K4 K5 K6 K7 K8 K9 K10 K11 K12 K13 K14 K15 K16 K17 K18 K19 K20 K21 K22 K23 K24 K25 K26 K27 K28 K29 K30 K31 K32 K33
>  K34 K35 K36 K37 K38 K39 K40 K41 K42 K43 K44 K45 K47 K48 K49 K50 K51 K52 K53 K54 K55 K56 K57 K58 K59 K60 K61 K62 K63 K64 K65 K66 K67 K68 K
> 69 K70 K71 K72 K73 K74 K75 K76 K77 K78 K79 K80 K81 K82 K83 K84 K85 K86 K87 K88 K89 K90 K91"
  3. global K1 = .1
  4.  global K2 = .2
  5.   global K3 = .3
  6.   global K4 = .4
  7.   global K5 = .5
  8.    global K6 = .6
  9.    global K7 = .7
 10.   global K8 = .8
 11.   global K9 = .9
 12.   global K10 = 1
 13.   global K11 = 1.1
 14.   global K12 = 1.2
 15.   global K13 = 1.3
 16.   global K14 = 1.4
 17.   global K15 = 1.5
 18.   global K16 = 1.6
 19.   global K17 = 1.7
 20.   global K18 = 1.8
 21.   global K19 = 1.9
 22.   global K20 = 2
 23.   global K21 = 2.1
 24.   global K22 = 2.2
 25.   global K23 = 2.3
 26.   global K24 = 2.4
 27.   global K25 = 2.5
 28.   global K26 = 2.6
 29.   global K27 = 2.7
 30.   global K28 = 2.8
 31.   global K29 = 2.9
 32.   global K30 =3
 33.   global K31 =3.2
 34.   global K32 =3.1
 35.   global K33 =3.3
 36.   global K34 =3.4
 37.   global K35 =3.5
 38.   global K36 =3.6
 39.   global K37 =3.7
 40.   global K38 =3.8
 41.   global K39 =3.9
 42.   global K40 =4
 43.   global K41 =4.1
 44.   global K42 =4.2
 45.  global K43 =4.3
 46.   global K44 =4.4
 47.   global K45 =4.5
 48.   global K47 =4.7
 49.   global K48 =4.8
 50.   global K49 =4.9
 51.   global K50 =5
 52.   global K51 =5.1
 53.   global K52 =5.2
 54.   global K53 =5.3
 55.   global K54 =5.4
 56.   global K55 =5.5
 57.   global K56 =5.6
 58.   global K57 =5.7
 59.   global K58 =5.8
 60.   global K59 =5.9
 61.   global K60 =6
 62.   global K61 =6.1
 63.   global K62 =6.2
 64.   global K63 =6.3
 65.   global K64 =6.4
 66.   global K65 =6.5
 67.   global K66 =6.6
 68.   global K67 =6.7
 69.   global K68 =6.8
 70.   global K69 =6.9
 71.   global K70 =7
 72.   global K71 =7.1
 73.   global K72 =7.2
 74.   global K73 =7.3
 75.   global K74 =7.4
 76.   global K75 =7.5
 77.   global K76 =7.6
 78.   global K77 =7.7
 79.   global K78 =7.8
 80.  global K79 =7.9
 81.   global K80 =8
 82.   global K81 =8.1
 83.   global K82 =8.2
 84.   global K83 =8.3
 85.   global K84 =8.4
 86.   global K85 =8.5
 87.   global K86 =8.6
 88.   global K87 =8.7
 89.   global K88 =8.8
 90.   global K89 =8.9
 91.   global K90 =9
 92. global K91 =9.1
 93. exit
 94. }
 95. replace `1'= (upstreamwaterquality04*exp(-$K1*aggdistadd)) + (($K2/($K1-$K2))*exp(-$K1*aggthirdfourthadd)*(exp(-$K2*seconddistadd)-exp(-$K1*seconddistadd))*mcavfoia04avgonejul) + (($K2/($K1-$K2))*exp(-$K1*fourthdistadd)*(exp(-$K2*thirddistadd)-exp(-$K1*thirddistadd))*mcavfoia04avgtwojul) + (($K2/($K1-$K2))*(exp(-$K2*fourthdistadd)-exp(-$K1*fourthdistadd))*mcavfoia04avgthreejul) +  ($K3*yeardum1) + ($K4*yeardum2) + ($K5*yeardum4) + ($K6*yeardum5) + ($K7*yeardum3) + ($K8*yeardum7) + ($K9*yeardum8) +($K10*yeardum9)+($K11*yeardum10)+($K12*stadum1) +($K13* stadum2)+($K14*stadum3)+($K15*stadum4)+($K16*stadum5)+($K17*stadum6)+($K18*stadum7)+($K19*stadum8)+($K20*stadum9)+($K21*stadum10)+ ($K22*stadum11)+($K23*stadum12)+($K24*stadum13)+($K25*stadum14)+($K26*stadum15)+($K27*stadum16)+($K28*stadum17)+($K29*stadum18)+($K30*stadum19)+($K31*stadum20)+($K32*stadum21)+($K33*stadum22)+($K34*stadum23)+($K35*stadum24)+($K36*stadum25)+($K37*stadum26)+($K38*stadum27)+($K39*stadum28)+($K40*s!
 tadum29)+($K41*stadum30)+($K42*stadum31)+($K43*stadum32)+($K44*stadum33)+($K45*stadum34)+($K47*stadum35)+($K48*stadum36)+($K49*stadum37)+($K50*stadum38)+($K51*stadum39)+($K52*stadum40)+($K53*stadum41)+($K54*stadum42)+($K55*stadum43)+($K56*stadum44)+($K57*stadum45)+($K58*stadum46)+($K59*stadum47)+($K60*stadum48)+($K61*stadum50)+($K62*stadum51)+($K63*stadum52)+($K64*stadum53)+($K65*stadum54)+($K66*stadum55)+($K67*stadum56)+($K68*stadum57)+($K69*stadum58)+($K70*stadum59)+($K71*stadum60)+($K72*stadum61)+($K73*stadum62)+($K74*stadum63)+($K75*stadum64)+($K76*stadum65)+($K77*stadum66)+($K78*stadum67)+($K79*stadum68)+($K80*stadum69)+($K81*stadum70)+($K82*stadum71)+($K83*stadum72)+($K84*stadum73)+($K85*stadum74)+($K86*stadum75)+($K87*stadum76)+($K88*yeardum11)+($K89*yeardum12)+($K90*yeardum13)+($K91*yeardum14)
 96. end

. drop if downstreamwaterqualityfoia04==.|upstreamwaterquality04==.
(3615 observations deleted)

. nl wqfinalnoseasonfinal2 downstreamwaterqualityfoia04
(obs = 9153)

Iteration 0:   residual SS =  341383.3
Iteration 1:   residual SS =  102316.7
Iteration 2:   residual SS =  49065.96
Iteration 3:   residual SS =   46477.6
Iteration 4:   residual SS =  29608.39
Iteration 5:   residual SS =  16176.47
Iteration 6:   residual SS =  15634.87
Iteration 7:   residual SS =  15634.77
Iteration 8:   residual SS =  15634.77
Iteration 9:   residual SS =  15634.77
Iteration 10:  residual SS =  15634.77
Iteration 11:  residual SS =  15634.77

      Source |       SS       df       MS            Number of obs =      9153
-------------+------------------------------         F( 90,  9063) =   5619.77
       Model |  872530.389    90   9694.7821         Prob > F      =    0.0000
    Residual |  15634.7659  9063  1.72512037         R-squared     =    0.9824
-------------+------------------------------         Adj R-squared =    0.9822
       Total |  888165.155  9153  97.0354151         Root MSE      =  1.313438
                                                     Res. dev.     =  30875.75
(wqfinalnoseasonfinal2)
------------------------------------------------------------------------------
downstrea~04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          K1 |   .0010741   .0002093     5.13   0.000     .0006639    .0014843
          K2 |   .0009496    .000181     5.25   0.000     .0005947    .0013044
          K3 |   .3428704   .0708687     4.84   0.000     .2039518     .481789
          K4 |    .271362   .0699095     3.88   0.000     .1343236    .4084004
          K5 |   .3148739    .066197     4.76   0.000     .1851129    .4446349
          K6 |   .4146427   .0663179     6.25   0.000     .2846447    .5446407
          K7 |   .3118678   .0675535     4.62   0.000     .1794477    .4442879
          K8 |    .422993    .067067     6.31   0.000     .2915266    .5544594
          K9 |   .3973913   .0675301     5.88   0.000     .2650171    .5297655
         K10 |   .4092157   .0668493     6.12   0.000     .2781759    .5402554
         K11 |   .4760093   .0687816     6.92   0.000     .3411819    .6108368
         K12 |  -.2038022   .2438044    -0.84   0.403    -.6817139    .2741095
         K13 |  -3.317183    .217564   -15.25   0.000    -3.743658   -2.890709
         K14 |   1.255814   .2146534     5.85   0.000     .8350452    1.676583
         K15 |   .1357352   .1379203     0.98   0.325    -.1346198    .4060902
         K16 |   -1.00154   .2406393    -4.16   0.000    -1.473248   -.5298328
         K17 |   .0865851   .1257938     0.69   0.491    -.1599992    .3331693
         K18 |    .135863    .127752     1.06   0.288    -.1145597    .3862858
         K19 |  -.7537097   .1213581    -6.21   0.000     -.991599   -.5158204
         K20 |   .2041277   .1179421     1.73   0.084    -.0270653    .4353208
         K21 |  -.0752475   .1219113    -0.62   0.537    -.3142213    .1637262
         K22 |  -.2495813    .113892    -2.19   0.028    -.4728352   -.0263273
         K23 |  -.5214178    .117565    -4.44   0.000    -.7518717   -.2909639
         K24 |   -.444463   .1136625    -3.91   0.000    -.6672671   -.2216589
         K25 |  -.1725613   .1301632    -1.33   0.185    -.4277107     .082588
         K26 |  -.2888296   .1128871    -2.56   0.011    -.5101138   -.0675453
         K27 |  -1.544702   .1247536   -12.38   0.000    -1.789247   -1.300157
         K28 |  -.6062011   .1247777    -4.86   0.000    -.8507935   -.3616086
         K29 |   .8729862   .1246386     7.00   0.000     .6286663    1.117306
         K30 |   .1170514   .2137103     0.55   0.584    -.3018691    .5359718
         K31 |  -.7954075   .1747776    -4.55   0.000    -1.138011   -.4528039
         K32 |  -.2563595   .1358445    -1.89   0.059    -.5226453    .0099263
         K33 |  -2.972842   .1478618   -20.11   0.000    -3.262685   -2.682999
         K34 |  -.4813647    .157364    -3.06   0.002    -.7898336   -.1728957
         K35 |  -.4834126   .1319989    -3.66   0.000    -.7421602    -.224665
         K36 |  -.7361197   .2377246    -3.10   0.002    -1.202114   -.2701258
         K37 |   -1.73866   .1499334   -11.60   0.000    -2.032563   -1.444756
         K38 |  -.6436632   .1425832    -4.51   0.000    -.9231583    -.364168
         K39 |   .5287935     .16607     3.18   0.001     .2032588    .8543282
         K40 |  -.8042813   .3131087    -2.57   0.010    -1.418045   -.1905175
         K41 |  -.0539605   .1156924    -0.47   0.641    -.2807438    .1728227
         K42 |  -.5749613   .1238141    -4.64   0.000    -.8176648   -.3322578
         K43 |  -.5077306   .1302183    -3.90   0.000    -.7629878   -.2524734
         K44 |  -.8878058    .134353    -6.61   0.000    -1.151168   -.6244436
         K45 |   .1383337   .1315046     1.05   0.293     -.119445    .3961125
         K47 |   .2372109    .205014     1.16   0.247    -.1646627    .6390846
         K48 |  -.3145575    .206682    -1.52   0.128    -.7197009     .090586
         K49 |   .0456258   .1128452     0.40   0.686    -.1755763    .2668279
         K50 |  -.2381294     .11368    -2.09   0.036    -.4609678    -.015291
         K51 |  -.7084642   .1323899    -5.35   0.000    -.9679782   -.4489501
         K52 |   -1.05861   .1488104    -7.11   0.000    -1.350312   -.7669077
         K53 |  -.3741486   .1654576    -2.26   0.024    -.6984828   -.0498144
         K54 |   -.816306   .1225121    -6.66   0.000    -1.056457   -.5761546
         K55 |  -.3895063   .1274319    -3.06   0.002    -.6393015   -.1397111
         K56 |   1.278173   .1211292    10.55   0.000     1.040733    1.515614
         K57 |  -.5613078   .1409231    -3.98   0.000    -.8375489   -.2850666
         K58 |   .3994558   .1116627     3.58   0.000     .1805718    .6183399
         K59 |  -1.135466   .1121881   -10.12   0.000     -1.35538   -.9155525
         K60 |   .2694433   .1150676     2.34   0.019     .0438849    .4950016
         K61 |     -2.483   .1337933   -18.56   0.000    -2.745265   -2.220735
         K62 |   .2642295   .1270201     2.08   0.038     .0152414    .5132176
         K63 |   .0098229   .1193733     0.08   0.934    -.2241758    .2438216
         K64 |   1.048413   .1193612     8.78   0.000     .8144384    1.282388
         K65 |  -.3798097   .1115507    -3.40   0.001    -.5984742   -.1611452
         K66 |   -.018244    .111156    -0.16   0.870    -.2361348    .1996468
         K67 |   -.224859   .1228402    -1.83   0.067    -.4656536    .0159355
         K68 |  -.1363394   .1236679    -1.10   0.270    -.3787564    .1060776
         K69 |   .0459337   .1479229     0.31   0.756    -.2440286    .3358959
         K70 |  -.9490123   .1133691    -8.37   0.000    -1.171241   -.7267833
         K71 |  -.9045401   .1151163    -7.86   0.000    -1.130194   -.6788861
         K72 |   .2384252   .1111493     2.15   0.032     .0205474     .456303
         K73 |  -.4776609   .1220676    -3.91   0.000    -.7169409   -.2383809
         K74 |  -.3299525   .1121138    -2.94   0.003    -.5497209   -.1101842
         K75 |   -.077161   .1118384    -0.69   0.490    -.2963896    .1420675
         K76 |  -.7747893   .1113143    -6.96   0.000    -.9929905   -.5565882
         K77 |  -.3216125   .1350793    -2.38   0.017    -.5863984   -.0568266
         K78 |   -.095592   .1868622    -0.51   0.609    -.4618842    .2707001
         K79 |  -.3051337   .1319826    -2.31   0.021    -.5638495    -.046418
         K80 |   2.371013   .1460216    16.24   0.000     2.084778    2.657248
         K81 |  -.3310636   .1389508    -2.38   0.017    -.6034386   -.0586887
         K82 |  -2.759564   .1907757   -14.46   0.000    -3.133527     -2.3856
         K83 |  -.5172954   .1279836    -4.04   0.000    -.7681722   -.2664186
         K84 |   .0662271   .1773992     0.37   0.709    -.2815153    .4139696
         K85 |  -1.267226   .1219258   -10.39   0.000    -1.506228   -1.028224
         K86 |  -.9715404   .1322481    -7.35   0.000    -1.230776   -.7123044
         K87 |   .0597408   .1348306     0.44   0.658    -.2045576    .3240391
         K88 |   .4231012   .0678804     6.23   0.000     .2900403    .5561621
         K89 |   .4362162   .0734967     5.94   0.000      .292146    .5802863
         K90 |   .4764204   .0781196     6.10   0.000     .3232883    .6295525
         K91 |   .4073448   .0768292     5.30   0.000     .2567423    .5579474
------------------------------------------------------------------------------
 (SEs, P values, CIs, and correlations are asymptotic approximations)

. predictnl bod1 = ($K2/($K1-$K2))*exp(-$K1*aggthirdfourthadd)*(exp(-$K2*seconddistadd)-exp(-$K1*seconddistadd)), se(std)
Warning: prediction doesn't vary with respect to e(b).

. set more on

.
end of do-file

. sum bod1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
        bod1 |      9153    .0079253    .0081954   .0000949    .043418

. sum std

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
         std |      9153           0           0          0          0


________________________________________
From: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] On Behalf Of Kit Baum [baum@bc.edu]
Sent: Saturday, October 25, 2008 5:26 PM
To: statalist@hsphsun2.harvard.edu
Subject: st: re: problem using predictnl after obtaining non-linear estimates

< >
Lopa said

I wrote a simple non-linear least squares program. Using the estimates
I am trying to find confidence intervals etc. for one of the
components of the equation. The error message I keep getting is:
"Warning: prediction doesn't vary with respect to e(b)." and it
calculates the predicted value but does not generate standard errors
etc.

Presumably you mean a program for use with -nl-. If you would show us
the program, we might be able to figure out what is (or isn't) going
on...

Kit Baum, Boston College Economics and DIW Berlin
http://ideas.repec.org/e/pba1.html
An Introduction to Modern Econometrics Using Stata:
http://www.stata-press.com/books/imeus.html


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