Code:
Code:
tnbreg PPE_tot Sex Age Educ_2 Educ_3 Educ_4 certification Off_farm_income live_in_farm Exp_AGRIC ProdVal_scale REGION2 REGION3 HHsz Cooperative Mines Cred_req Landsize_ha size_type if PPE_tot!=0 , vce(robust) tnbreg PPE_tot Sex Age Educ_2 Educ_3 Educ_4 certification Off_farm_income live_in_farm Exp_AGRIC cereals forage fruit_tree hoticulture_crop industrial_crop legumes_crop other_crop root_crop REGION2 REGION3 HHsz Cooperative Mines Cred_req Landsize_ha size_type if PPE_tot!=0 , vce(robust)
I had this working but I seem not to be confident with the results considering the observations reported.
Code
[tnbreg PPE_tot Sex Age Educ_2 Educ_3 Educ_4 certification Off_farm_income live_in_farm Exp_AGRIC total REGION2 REGION3 HHsz Cooperative Mines Cred_req Landsize_ha size_type if PPE_tot>0 , vce(robust)][/CODE]
Html
[
. tnbreg PPE_tot Sex Age Educ_2 Educ_3 Educ_4 certification Off_farm_income live_in_farm | Exp_AGRIC | total | REGION2 | REGION3 | HHsz | Cooperative | Mines | Cred_req | Landsize_ha | size_type | if | PP |
> E_tot>0 , vce(robust) | ||||||||||||
Fitting truncated Poisson model: | ||||||||||||
Iteration 0: log pseudolikelihood = -11077.306 | ||||||||||||
Iteration 1: log pseudolikelihood = -10645.231 | ||||||||||||
Iteration 2: log pseudolikelihood = -10642.535 | ||||||||||||
Iteration 3: log pseudolikelihood = -10642.534 | ||||||||||||
Iteration 4: log pseudolikelihood = -10642.534 | ||||||||||||
Fitting constant-only model: | ||||||||||||
Iteration 0: log pseudolikelihood = -12366.73 | ||||||||||||
Iteration 1: log pseudolikelihood = -11752.445 (not concave) | ||||||||||||
Iteration 2: log pseudolikelihood = -11203.252 (not concave) | ||||||||||||
Iteration 3: log pseudolikelihood = -11059.054 | ||||||||||||
Iteration 4: log pseudolikelihood = -10905.429 | ||||||||||||
Iteration 5: log pseudolikelihood = -10816.88 | ||||||||||||
Iteration 6: log pseudolikelihood = -10794.809 | ||||||||||||
Iteration 7: log pseudolikelihood = -10789.092 | ||||||||||||
Iteration 8: log pseudolikelihood = -10787.261 | ||||||||||||
Iteration 9: log pseudolikelihood = -10786.842 | ||||||||||||
Iteration 10: log pseudolikelihood = -10786.746 | ||||||||||||
Iteration 11: log pseudolikelihood = -10786.724 | ||||||||||||
Iteration 12: log pseudolikelihood = -10786.719 | ||||||||||||
Iteration 13: log pseudolikelihood = -10786.718 | ||||||||||||
Iteration 14: log pseudolikelihood = -10786.718 | ||||||||||||
Iteration 15: log pseudolikelihood = -10786.718 (not concave) | ||||||||||||
Iteration 16: log pseudolikelihood = -10786.717 | ||||||||||||
Fitting full model: | ||||||||||||
Iteration 0: log pseudolikelihood = -10644.236 | ||||||||||||
Iteration 1: log pseudolikelihood = -10642.534 (not concave) | ||||||||||||
Iteration 2: log pseudolikelihood = -10642.534 (backed up) | ||||||||||||
Iteration 3: log pseudolikelihood = -10642.534 | ||||||||||||
Truncated negative binomial regression Number of obs = 8,739 | ||||||||||||
Truncation point: 0 Wald chi2(18) = 339.96 | ||||||||||||
Dispersion = mean Prob > chi2 = 0.0000 | ||||||||||||
Log pseudolikelihood = -10642.534 Pseudo R2 = 0.0134 | ||||||||||||
Robust | ||||||||||||
PPE_tot Coef. Std. Err. z P>z [95% Conf. Interval] | ||||||||||||
Sex .0120278 .0247843 0.49 0.627 -.0365486 .0606042 | ||||||||||||
Age .0026536 .0010996 2.41 0.016 .0004984 .0048089 | ||||||||||||
Educ_2 .1730886 .052353 3.31 0.001 .0704785 .2756987 | ||||||||||||
Educ_3 .359826 .0550719 6.53 0.000 .2518871 .4677649 | ||||||||||||
Educ_4 .5183198 .0586295 8.84 0.000 .4034082 .6332315 | ||||||||||||
certification .0844712 .1217221 0.69 0.488 -.1540997 .3230421 | ||||||||||||
Off_farm_income -.0061196 .0225044 -0.27 0.786 -.0502274 .0379881 | ||||||||||||
live_in_farm .0524648 .0217992 2.41 0.016 .0097392 .0951903 | ||||||||||||
Exp_AGRIC .001178 .0010712 1.10 0.271 -.0009214 .0032775 | ||||||||||||
total 4.83e-07 1.29e-07 3.74 0.000 2.30e-07 7.37e-07 | ||||||||||||
REGION2 .0104917 .0250771 0.42 0.676 -.0386585 .059642 | ||||||||||||
REGION3 .0381498 .0351378 1.09 0.278 -.030719 .1070186 | ||||||||||||
HHsz -.0024897 .00602 -0.41 0.679 -.0142888 .0093094 | ||||||||||||
Cooperative .1868915 .0350111 5.34 0.000 .118271 .255512 | ||||||||||||
Mines .1494356 .0340718 4.39 0.000 .0826562 .2162151 | ||||||||||||
Cred_req .1213029 .0270832 4.48 0.000 .0682207 .1743851 | ||||||||||||
Landsize_ha -.0005133 .000837 -0.61 0.540 -.0021538 .0011272 | ||||||||||||
size_type .1963899 .1406194 1.40 0.163 -.0792191 .4719988 | ||||||||||||
_cons -.4127863 .1636636 -2.52 0.012 -.733561 -.0920116 | ||||||||||||
/lnalpha -36.2097 . . . | ||||||||||||
alpha 1.88e-16 . . . | ||||||||||||
How do i go about it or resolve it.
0 Response to tnbreg does not converge
Post a Comment