Dear fellow Stata users,
i am running a mecloglog model in Stata 17 estimating the transition probabilities of youth from school to vocational education. It is district-level data embedded in greater, local labor market regions, that is why I am using the multilevel. To account for time dependencies my (main) explanatory variable (share_naa_high, a measure for competition depiciting the share of high skilled individuals in a region) is interacted with time (squared), please find my command and regression results below.
I would like to know, if there is any straightforward interpretation of the coefficients in such a model? Generally of the covariates but specifically here also the one from the interaction c.month_std#c.month_std#c.share_naa_high_std ( -.0663424), as well as if there is any useful interpretation of the singular covariate share_naa_high_std | -.0235111 that is automatically also included when calculating the interaction term.
My question is potentially rather basic, but I am having trouble finding literature on meclolog or cloglog in general online. I find it mostly mentioned as an alternative to logit etc. but without getting into more detail, especially in terms of regression results. Therefore I would also be happy about any recommendations on textbooks/literature/lectures online etc.
Thank you ver much,
Helen
i am running a mecloglog model in Stata 17 estimating the transition probabilities of youth from school to vocational education. It is district-level data embedded in greater, local labor market regions, that is why I am using the multilevel. To account for time dependencies my (main) explanatory variable (share_naa_high, a measure for competition depiciting the share of high skilled individuals in a region) is interacted with time (squared), please find my command and regression results below.
I would like to know, if there is any straightforward interpretation of the coefficients in such a model? Generally of the covariates but specifically here also the one from the interaction c.month_std#c.month_std#c.share_naa_high_std ( -.0663424), as well as if there is any useful interpretation of the singular covariate share_naa_high_std | -.0235111 that is automatically also included when calculating the interaction term.
My question is potentially rather basic, but I am having trouble finding literature on meclolog or cloglog in general online. I find it mostly mentioned as an alternative to logit etc. but without getting into more detail, especially in terms of regression results. Therefore I would also be happy about any recommendations on textbooks/literature/lectures online etc.
Thank you ver much,
Helen
Code:
mi estimate, cmdok: mecloglog des2 dig2_std subp_std c.month_std##c.month_std##c.share_naa_high_std $cont || ram:
Multiple-imputation estimates Imputations = 30
Mixed-effects cloglog regression Number of obs = 31,413
Average RVI = 0.0163
Largest FMI = 0.1390
DF adjustment: Large sample DF: min = 1,525.20
avg = 2.07e+08
max = 2.36e+09
Model F test: Equal FMI F( 14, 1.2e+06) = 53.97
Within VCE type: OIM Prob > F = 0.0000
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des2 | Coefficient Std. err. t P>|t| [95% conf. interval]
---------------------------------------------+----------------------------------------------------------------
dig2_std | .1385704 .0391823 3.54 0.000 .0617746 .2153662
subp_std | .0559094 .0441674 1.27 0.206 -.0306571 .142476
month_std | -.7366246 .0310888 -23.69 0.000 -.7975575 -.6756917
|
c.month_std#c.month_std | .4431871 .0283443 15.64 0.000 .3876332 .498741
|
share_naa_high_std | -.0235111 .0667418 -0.35 0.725 -.1543225 .1073004
|
c.month_std#c.share_naa_high_std | .0467641 .0305467 1.53 0.126 -.0131063 .1066346
|
c.month_std#c.month_std#c.share_naa_high_std | -.0663424 .0280888 -2.36 0.018 -.1213955 -.0112893
|
mig | -.2895376 .0618225 -4.68 0.000 -.4107121 -.168363
female | -.2595704 .05484 -4.73 0.000 -.3670549 -.1520859
gpa_std | -.1108702 .0307685 -3.60 0.000 -.1712232 -.0505172
books_std | .0612342 .0281863 2.17 0.030 .0059825 .1164858
sltyp_13 | .0177168 .0865387 0.20 0.838 -.1518959 .1873295
|
cohort_agg |
2 | -.2329763 .1035105 -2.25 0.024 -.4358533 -.0300994
3 | -.3990874 .3874382 -1.03 0.303 -1.158452 .3602776
|
_cons | -3.445424 .0786077 -43.83 0.000 -3.599492 -3.291356
---------------------------------------------+----------------------------------------------------------------
var(_cons[ram])| .1268323 .051242 .0574556 .2799801
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