Hi all,
I have a data set over a period 2008-2017. I estimated a gmm model based on this time period to obtain own and cross acreage and price elasticies. The dependent variable is the log of the acreage of the crop. In the next step, I need to predict the log of the acreage for year 2018. To do this I used the average of the value of each variable during 2008-2017 for the year that I need to predict the log of acreage except price. For the price I used the half of the minimum of the observed price during 2008-2017. In this case I am trying to simulate a shock in the economy that drops the price of the crop. I used the following command:

generate logacr=log(acr)
generate logacrl=log(acrl)
generate logpl=log(pl)
generate logpi=log(pi)

xtabond2 logacr l.logacr l.logacrl logpl fzi fui logpi gdd pop ppt year dev if year<11, gmm(logacr ,collapse) iv(gdd logpl fzi fui ppt year dev) small robust
predict logacr_hat if year>10

So, I want to predict logacrl for year 11. Now I think since I am predicting linearly and am using the half of the minimum price (pl), the predicted acreage that I get should be less than any other historically observed acreage duruing 2008-2017. But the results dont's show this.
Am I wrong? the results don't show what I expecetd. Can anyone help me on this please. Hope I am clear enough.

Thank you.


The results are as following:

Dynamic panel-data estimation, one-step system GMM
------------------------------------------------------------------------------
Group variable: coun_num Number of obs = 108
Time variable : year Number of groups = 12
Number of instruments = 31 Obs per group: min = 9
F(9, 11) = 258.29 avg = 9.00
Prob > F = 0.000 max = 9
------------------------------------------------------------------------------
| Robust
logacrl | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
logacrl |
L1. | .230327 .1489105 1.55 0.150 -.0974228 .5580768
|
logpl | .1325983 .4238889 0.31 0.760 -.8003748 1.065571
|
logacr |
L1. | .5866026 .3187713 1.84 0.093 -.1150084 1.288214
|
logpi | -1.409517 .3515745 -4.01 0.002 -2.183327 -.6357066
fzi | -.0019445 .0012559 -1.55 0.150 -.0047088 .0008197
gdd | .025999 .0348142 0.75 0.471 -.0506265 .1026244
ppt | -.0027826 .0082187 -0.34 0.741 -.0208718 .0153066
pop | -.0004244 .000487 -0.87 0.402 -.0014962 .0006474
year | .025035 .0289901 0.86 0.406 -.0387717 .0888417
_cons | 6.875543 2.696529 2.55 0.027 .9405235 12.81056
------------------------------------------------------------------------------
Instruments for first differences equation
Standard
D.(gdd pop gdd fui year)
GMM-type (missing=0, separate instruments for each period unless collapsed)
L(1/10).(logacrl logpl fzi) collapsed
Instruments for levels equation
Standard
gdd pop gdd fui year
_cons
GMM-type (missing=0, separate instruments for each period unless collapsed)
D.(logacrl logpl fzi) collapsed
------------------------------------------------------------------------------
Arellano-Bond test for AR(1) in first differences: z = -1.70 Pr > z = 0.089
Arellano-Bond test for AR(2) in first differences: z = 1.28 Pr > z = 0.201
------------------------------------------------------------------------------
Sargan test of overid. restrictions: chi2(21) = 61.99 Prob > chi2 = 0.000
(Not robust, but not weakened by many instruments.)
Hansen test of overid. restrictions: chi2(21) = 0.93 Prob > chi2 = 1.000


And the the data (historical and predicted) looks like this:
county coun_num year acrl acr pl pi ppt area pop tmin tmean gdd tmax fui fzi dev logacr logacrl logpl logpi logacr_hat exp_logacr_hat logacrl_hat exp_logacrl_hat
San-Diego 8 1 580 4293 86.07 115.25 22.14 4,206.63 725.3455 48.9 62.9 22.9 76.9 267.32 355.82 28 8.364741 6.363028 4.455161 4.747104
San-Diego 8 2 580 4650 66.83 128.52 12.76 4,206.63 731.613 48.6 62.4 22.4 76.2 177.75 250.15 27.6 8.444622 6.363028 4.202152 4.856084
San-Diego 8 3 575 4184 68.27 122.8 32.79 4,206.63 737.1859 47 60.2 20.2 73.3 220.56 229.25 26.3 8.339023 6.35437 4.22347 4.810557
San-Diego 8 4 575 4355 55.69 130.66 17 4,206.63 745.4806 46.9 60.4 20.4 73.8 281.69 298.51 26.9 8.37908 6.35437 4.019801 4.872599
San-Diego 8 5 517 3334 115.81 317.9 13.88 4,206.63 754.5223 48.6 62.5 22.5 76.4 279.72 302.69 27.8 8.111928 6.248043 4.751951 5.761737
San-Diego 8 6 84 3420 85.75 309.47 9.76 4,206.63 763.7608 48.3 61.7 21.7 75.1 277.18 288.66 26.8 8.137396 4.430817 4.451436 5.734861
San-Diego 8 7 580 2860 76.45 301.03 11.96 4,206.63 772.4171 50.3 63.7 23.7 77.2 275.77 282.09 26.9 7.958577 6.363028 4.336637 5.70721
San-Diego 8 8 577.5 2050 67.55 304.54 17.74 4,206.63 778.7462 49.9 62.5 22.5 75.1 178.87 260.3 25.2 7.625595 6.358708 4.212868 5.718802
San-Diego 8 9 575 2132 100.78 276.86 19.68 4,206.63 783.8108 49.6 62.6 22.6 75.6 156.62 214.33 26 7.664816 6.35437 4.61294 5.623512
San-Diego 8 10 576.25 1819 81.1 323.42 19.63 4,206.63 789.3223 50.3 63.2 23.2 76.1 178.03 197.61 25.8 7.506042 6.356542 4.395683 5.778952
San-Diego 8 11 521.975 3309.7 27.845 233.045 17.734 4206.63 758.2204 48.84 62.21 22.21 75.57 229.351 267.941 26.73 8.104612 6.25762 3.326653 5.451231 9.503097 13401.16 5.460869 235.3018
San-Luis 9 1 7588 31260 126.12 124.63 21.18 3,298.56 80.89681 39.8 57.7 17.7 75.5 267.32 355.82 35.7 10.35009 8.934323 4.837234 4.825349
San-Luis 9 2 7475 24844 169.68 147.61 17.87 3,298.56 81.3167 40.3 57.7 17.7 75.1 177.75 250.15 34.8 10.12037 8.919319 5.133914 4.994574
San-Luis 9 3 4590 22213 113.45 113.41 33.67 3,298.56 81.56408 40.7 56.7 16.7 72.8 220.56 229.25 32.1 10.00843 8.431635 4.731362 4.731009
San-Luis 9 4 5140 18795 106.8 110.76 21.61 3,298.56 81.97547 40.5 56.8 16.8 73 281.69 298.51 32.5 9.841346 8.544808 4.670958 4.707366
San-Luis 9 5 3650 16501 104.42 113.63 18.11 3,298.56 82.65516 43.3 60.5 20.5 77.7 279.72 302.69 34.4 9.711176 8.202482 4.648421 4.732947
San-Luis 9 6 3970 17836 112.35 141.06 4.36 3,298.56 83.06413 42.4 60.2 20.2 77.9 277.18 288.66 35.5 9.788974 8.286521 4.721619 4.949185
San-Luis 9 7 4471 17089 130.56 126.22 15.43 3,298.56 83.75685 45.6 62.7 22.7 79.9 275.77 282.09 34.3 9.74619 8.405368 4.871833 4.838027
San-Luis 9 8 5140 16256 156.95 179.98 9.64 3,298.56 83.93329 43.2 60.5 20.5 77.8 178.87 260.3 34.6 9.696218 8.544808 5.055927 5.192846
San-Luis 9 9 4090 15036 133.82 133.26 20.19 3,298.56 84.38955 41.8 59 19 76.1 156.62 214.33 34.3 9.618202 8.3163 4.896496 4.892302
San-Luis 9 10 3540 13194 156.15 174.58 28.54 3,298.56 84.48535 42.4 59.4 19.4 76.4 178.03 197.61 34 9.487517 8.171882 5.050817 5.162383
San-Luis 9 11 4965.4 19302.4 52.21 136.514 19.06 3,298.56 82.80374 42 59.12 19.12 76.22 229.351 267.941 34.22 9.867985 8.510249 3.955274 4.916427 10.63043 41375.08 8.081109 3232.817