Hi,
can anyone help me on how to deal with the problem of missing F-test value?
Thanks
can anyone help me on how to deal with the problem of missing F-test value?
Code:
Multiple-imputation estimates Imputations = 20
Tobit regression Number of obs = 1,985
Average RVI = 0.0000
Largest FMI = 0.0000
DF: min = 7.49e+65
avg = 7.49e+65
DF adjustment: Large sample max = .
F( 17, .) = .
Within VCE type: Robust Prob > F = .
(Within VCE adjusted for 183 clusters in Municipality)
-----------------------------------------------------------------------------------
lnEffectMW | Coef. Std. Err. t P>|t| [95% Conf. Interval]
------------------+----------------------------------------------------------------
lnIncome | .1713888 1.330814 0.13 0.898 -2.436958 2.779736
lnArea | -10.9632 18.99225 -0.58 0.564 -48.18732 26.26092
lnPop | -11.0056 18.96456 -0.58 0.562 -48.17546 26.16425
lnAreaPop | 11.16809 18.90128 0.59 0.555 -25.87774 48.21393
ln_UnempL5 | -.1357295 .1727421 -0.79 0.432 -.4742978 .2028388
RedBlockParty | .0112927 .1538175 0.07 0.941 -.2901841 .3127694
lnAgeGrp1 | -1.618337 .7806243 -2.07 0.038 -3.148332 -.0883411
lnAgeGrp2 | 1.118265 .5289295 2.11 0.034 .0815821 2.154947
lnAgeGrp3 | -.306101 .9195161 -0.33 0.739 -2.10832 1.496117
lnAgeGrp4 | -.2789467 1.115882 -0.25 0.803 -2.466035 1.908141
lnAgeGrp5 | .4830414 .8223909 0.59 0.557 -1.128815 2.094898
lnAgeGrp6 | -1.380766 .8250489 -1.67 0.094 -2.997832 .2362999
lnAgeGrp7 | .0544808 .8709994 0.06 0.950 -1.652647 1.761608
lnAgeGrp8 | 1.59106 .5788295 2.75 0.006 .4565748 2.725545
MM | -.2155857 .4176816 -0.52 0.606 -1.034227 .6030553
CM | .4188016 .1845671 2.27 0.023 .0570567 .7805466
Year | .1478967 .0588115 2.51 0.012 .0326283 .2631652
_cons | -293.4682 118.6287 -2.47 0.013 -525.9762 -60.96013
------------------+----------------------------------------------------------------
var(e.lnEffectMW)| 1.34662 .0945428 1.173504 1.545275
-----------------------------------------------------------------------------------
Code:
Refining starting values:
Grid node 0: log likelihood = -3110.715
Fitting full model:
Iteration 0: log pseudolikelihood = -3110.715
Iteration 1: log pseudolikelihood = -3110.6653
Iteration 2: log pseudolikelihood = -3110.6653
Tobit regression Number of obs = 1,985
Uncensored = 1,978
Limits: lower = 0 Left-censored = 7
upper = +inf Right-censored = 0
Wald chi2(17) = 308.92
Log pseudolikelihood = -3110.6653 Prob > chi2 = 0.0000
(Std. Err. adjusted for 183 clusters in Municipality)
-----------------------------------------------------------------------------------
| Robust
lnEffectMW | Coef. Std. Err. z P>|z| [95% Conf. Interval]
------------------+----------------------------------------------------------------
lnIncome | .1713888 1.330814 0.13 0.898 -2.436958 2.779736
lnArea | -10.9632 18.99225 -0.58 0.564 -48.18732 26.26092
lnPop | -11.0056 18.96456 -0.58 0.562 -48.17546 26.16425
lnAreaPop | 11.16809 18.90128 0.59 0.555 -25.87774 48.21393
ln_UnempL5 | -.1357295 .1727421 -0.79 0.432 -.4742978 .2028388
RedBlockParty | .0112927 .1538175 0.07 0.941 -.2901841 .3127694
lnAgeGrp1 | -1.618337 .7806243 -2.07 0.038 -3.148332 -.0883411
lnAgeGrp2 | 1.118265 .5289295 2.11 0.034 .0815821 2.154947
lnAgeGrp3 | -.306101 .9195161 -0.33 0.739 -2.10832 1.496117
lnAgeGrp4 | -.2789467 1.115882 -0.25 0.803 -2.466035 1.908141
lnAgeGrp5 | .4830414 .8223909 0.59 0.557 -1.128815 2.094898
lnAgeGrp6 | -1.380766 .8250489 -1.67 0.094 -2.997832 .2362999
lnAgeGrp7 | .0544808 .8709994 0.06 0.950 -1.652647 1.761608
lnAgeGrp8 | 1.59106 .5788295 2.75 0.006 .4565748 2.725545
MM | -.2155857 .4176816 -0.52 0.606 -1.034227 .6030552
CM | .4188016 .1845671 2.27 0.023 .0570567 .7805466
Year | .1478967 .0588115 2.51 0.012 .0326283 .2631652
_cons | -293.4682 118.6287 -2.47 0.013 -525.9762 -60.96013
------------------+----------------------------------------------------------------
var(e.lnEffectMW)| 1.34662 .0945428 1.173504 1.545275
-----------------------------------------------------------------------------------
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