Dear all,
I want to calculate the average treatment effect within panel data with a panel of 3 time points and about 178 individuals for ordinal outcomes (scale of 4 or 5). The reason I mainly consider fixed-effects is that the treatment and the control group were not selected at random. Therefore I am concerned about unobserved heterogeneity.
My first approach is the hybrid model using the stata command 'xthybrid' after 'xtset id wave' in the paper from Schunck and Perales (2017). In general that worked pretty well, but I had problems with 'xtsum[...] if e(sample)' as there appears a table without any numbers and "xtgraph[...] if e(sample)" where the error message "__000002 not found" ocurred.
Therefore my second approach is the recommendation of Allison (2009) to generate variables for within and between cluster effects on my own using, e.g.
egen M_a = mean(a), by(id)
gen F_a = a - M_a
Afterwards I ran the regression with the generated variables with meglm using Stata/SE 15.1.
So my main question is:
1) What are the main differences between the calculation with
xthybrid[...], family(ordinal) link(logit) vce(robust) clusterid(id) full
and
meglm[...], family(ordinal) link(logit) vce(robust) ||id:
because the results are very close to each other but somewhat different (see Screenshot). What are the reasons/issues I have to consider, if I want to decide which calculation is more appropriate in a certain case?
2) Are there any other recommendations how I should calculate Fixed-Effects for ordinal variables with a panel of 3 time points and about 178 individuals? The only suggestion apart from xthybrid and meglm I found so far
Thank you very much in advance and all the best,
Sigi
P.S: Sorry for the Screenshot. "W_" and "F_" represent the within effect and "B_" and "M_" the between effect.
Literature:
Allison, P. D. (2009). Fixed effects regression models (Vol. 160). SAGE publications.
Schunck, R., & Perales, F. (2017). Within-and between-cluster effects in generalized linear mixed models: A discussion of approaches and the xthybrid command. Stata Journal, 17(1), 89-115.
I want to calculate the average treatment effect within panel data with a panel of 3 time points and about 178 individuals for ordinal outcomes (scale of 4 or 5). The reason I mainly consider fixed-effects is that the treatment and the control group were not selected at random. Therefore I am concerned about unobserved heterogeneity.
My first approach is the hybrid model using the stata command 'xthybrid' after 'xtset id wave' in the paper from Schunck and Perales (2017). In general that worked pretty well, but I had problems with 'xtsum[...] if e(sample)' as there appears a table without any numbers and "xtgraph[...] if e(sample)" where the error message "__000002 not found" ocurred.
Therefore my second approach is the recommendation of Allison (2009) to generate variables for within and between cluster effects on my own using, e.g.
egen M_a = mean(a), by(id)
gen F_a = a - M_a
Afterwards I ran the regression with the generated variables with meglm using Stata/SE 15.1.
So my main question is:
1) What are the main differences between the calculation with
xthybrid[...], family(ordinal) link(logit) vce(robust) clusterid(id) full
and
meglm[...], family(ordinal) link(logit) vce(robust) ||id:
because the results are very close to each other but somewhat different (see Screenshot). What are the reasons/issues I have to consider, if I want to decide which calculation is more appropriate in a certain case?
2) Are there any other recommendations how I should calculate Fixed-Effects for ordinal variables with a panel of 3 time points and about 178 individuals? The only suggestion apart from xthybrid and meglm I found so far
Thank you very much in advance and all the best,
Sigi
P.S: Sorry for the Screenshot. "W_" and "F_" represent the within effect and "B_" and "M_" the between effect.
Literature:
Allison, P. D. (2009). Fixed effects regression models (Vol. 160). SAGE publications.
Schunck, R., & Perales, F. (2017). Within-and between-cluster effects in generalized linear mixed models: A discussion of approaches and the xthybrid command. Stata Journal, 17(1), 89-115.
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