Hello Statalist,
I am working on a model that can explain loss aversion in saving behaviour using a household survey (Household Finance and Consumption Survey from the European Central Bank) with 2 waves. To do so, I use an income binary variable that takes into account if the income over the last 12 months was unusually high or low compared to what the respondent would expect in a normal year. The dependent variable is a binary variable that considers if the respondent saved or not during this period. Along with these variables, I introduce a set of control variables such as age, level of education, labour status, tenure status and so on.
I would like to compare these results by countries (7 countries) using a multilevel mixedeffect logit, and my question is consisted of whether I could use a mixedeffect logistic regression together with panel data. To do so, I include two random intercepts that varies from one country to the next and also by waves (time variable). I am also using multiple imputation to deal with the issue of missing values in the household data. I set up my dataset according to the command "mi xtset householdid wave". The HFCS has only two waves just right now.
mi estimate, vceok esampvaryok: xtmelogit saving20 highincome lowincome gender i.pa0200 dh0001 ra0300 ownallresidence rentedresidence employee unemployed retiree marriedstatus singlestatus highrisk  sa0100:  wave: , intpoints(15)
Another question is about the interpretation of these results. What can I say about the coefficients if I want to compare them. For instance, I am interested in explaining that the positive effect of high income on the probability of saving is higher than the negative effect of low income on the probability of saving. Can I use average marginal effect with xtmelogit? Or it is better other options such as oddratios, to interpret the coefficients?
Mixedeffects logistic regression Number of obs = 23,722

 No. of Observations per Group Integration
Group Variable  Groups Minimum Average Maximum Points
+
country  7 1,209 3,388.9 7,407 15
wave  14 601 1,694.4 3,711 15

Wald chi2(16) = 1169.72
Log likelihood = 14931.238 Prob > chi2 = 0.0000

saving20  Coef. Std. Err. z P>z [95% Conf. Interval]
+
highincome  .3423776 .053509 6.40 0.000 .2375018 .4472534
lowincome  .6060649 .0366153 16.55 0.000 .6778296 .5343002
gender  .0774088 .0346698 2.23 0.026 .0094573 .1453604

pa0200 
2  .0847355 .0489974 1.73 0.084 .0112976 .1807686
3  .1830587 .0462057 3.96 0.000 .0924972 .2736202
5  .5877664 .0457286 12.85 0.000 .4981399 .6773928

dh0001  .1129852 .0151901 7.44 0.000 .1427572 .0832132
agebracket  .004263 .001578 2.70 0.007 .0011702 .0073559
ownallresidence  .1575485 .0555991 2.83 0.005 .0485764 .2665207
rentedresidence  .3119736 .0640343 4.87 0.000 .4374785 .1864687
employee  .2644405 .0665266 3.97 0.000 .1340508 .3948301
unemployed  .4973625 .1020421 4.87 0.000 .6973613 .2973636
retiree  .0380407 .0649416 0.59 0.558 .1653238 .0892425
marriedstatus  .1910295 .0440335 4.34 0.000 .1047254 .2773337
singlestatus  .0132219 .0543512 0.24 0.808 .0933046 .1197483
highrisk  .1421069 .0569081 2.50 0.013 .030569 .2536448
_cons  .8839132 .234231 3.77 0.000 1.342997 .4248289


Randomeffects Parameters  Estimate Std. Err. [95% Conf. Interval]
+
country: Identity 
sd(_cons)  .4411826 .1596276 .2170895 .8965986
+
wave: Identity 
sd(_cons)  .3433583 .0941693 .2005846 .5877567

LR test vs. logistic model: chi2(2) = 1160.88 Prob > chi2 = 0.0000
Note: LR test is conservative and provided only for reference.
Note: Loglikelihood calculations are based on the Laplacian approximation.
Thank you so much!!
Gonzalo.
I am working on a model that can explain loss aversion in saving behaviour using a household survey (Household Finance and Consumption Survey from the European Central Bank) with 2 waves. To do so, I use an income binary variable that takes into account if the income over the last 12 months was unusually high or low compared to what the respondent would expect in a normal year. The dependent variable is a binary variable that considers if the respondent saved or not during this period. Along with these variables, I introduce a set of control variables such as age, level of education, labour status, tenure status and so on.
I would like to compare these results by countries (7 countries) using a multilevel mixedeffect logit, and my question is consisted of whether I could use a mixedeffect logistic regression together with panel data. To do so, I include two random intercepts that varies from one country to the next and also by waves (time variable). I am also using multiple imputation to deal with the issue of missing values in the household data. I set up my dataset according to the command "mi xtset householdid wave". The HFCS has only two waves just right now.
mi estimate, vceok esampvaryok: xtmelogit saving20 highincome lowincome gender i.pa0200 dh0001 ra0300 ownallresidence rentedresidence employee unemployed retiree marriedstatus singlestatus highrisk  sa0100:  wave: , intpoints(15)
Another question is about the interpretation of these results. What can I say about the coefficients if I want to compare them. For instance, I am interested in explaining that the positive effect of high income on the probability of saving is higher than the negative effect of low income on the probability of saving. Can I use average marginal effect with xtmelogit? Or it is better other options such as oddratios, to interpret the coefficients?
Mixedeffects logistic regression Number of obs = 23,722

 No. of Observations per Group Integration
Group Variable  Groups Minimum Average Maximum Points
+
country  7 1,209 3,388.9 7,407 15
wave  14 601 1,694.4 3,711 15

Wald chi2(16) = 1169.72
Log likelihood = 14931.238 Prob > chi2 = 0.0000

saving20  Coef. Std. Err. z P>z [95% Conf. Interval]
+
highincome  .3423776 .053509 6.40 0.000 .2375018 .4472534
lowincome  .6060649 .0366153 16.55 0.000 .6778296 .5343002
gender  .0774088 .0346698 2.23 0.026 .0094573 .1453604

pa0200 
2  .0847355 .0489974 1.73 0.084 .0112976 .1807686
3  .1830587 .0462057 3.96 0.000 .0924972 .2736202
5  .5877664 .0457286 12.85 0.000 .4981399 .6773928

dh0001  .1129852 .0151901 7.44 0.000 .1427572 .0832132
agebracket  .004263 .001578 2.70 0.007 .0011702 .0073559
ownallresidence  .1575485 .0555991 2.83 0.005 .0485764 .2665207
rentedresidence  .3119736 .0640343 4.87 0.000 .4374785 .1864687
employee  .2644405 .0665266 3.97 0.000 .1340508 .3948301
unemployed  .4973625 .1020421 4.87 0.000 .6973613 .2973636
retiree  .0380407 .0649416 0.59 0.558 .1653238 .0892425
marriedstatus  .1910295 .0440335 4.34 0.000 .1047254 .2773337
singlestatus  .0132219 .0543512 0.24 0.808 .0933046 .1197483
highrisk  .1421069 .0569081 2.50 0.013 .030569 .2536448
_cons  .8839132 .234231 3.77 0.000 1.342997 .4248289


Randomeffects Parameters  Estimate Std. Err. [95% Conf. Interval]
+
country: Identity 
sd(_cons)  .4411826 .1596276 .2170895 .8965986
+
wave: Identity 
sd(_cons)  .3433583 .0941693 .2005846 .5877567

LR test vs. logistic model: chi2(2) = 1160.88 Prob > chi2 = 0.0000
Note: LR test is conservative and provided only for reference.
Note: Loglikelihood calculations are based on the Laplacian approximation.
Thank you so much!!
Gonzalo.
Comment