st: elasticity and odds ratios in xtmelogit

[Bosco Rowland <bosco.rowland@deakin.edu.au>]

Hi Stata list,

I have a  question pertaining to elasitcities in an xtmelogit model.

I have run an xtmelogit regression with imputed data.  I have identified a significant interaction with two continuous variables:

mi estimate, or var :  xtmelogit ind14_3e  saq05 saq06  seq09_r  PRfud ind10_8a ind14_3a metro_reg c.saq01##c.package_den || lga1:
it estimates consumption in last 30 days(yes or no: ind14_3e) 
package density (package_den)  and age (saq01) are the key IVs, the others are covariates,, LGA is the cluster variable


I have used margins (ey/ex) to understand the interaction, however, as the data uses imputed data I have had to write a wrapper program based on the following information on the UCLA website
http://www.ats.ucla.edu/stat/stata/faq/ologit_mi_marginsplot.htm

The basic margins command is based on the following
margins, eyex(package_den) at(saq01= (2 (1) 7)) atmeans asbalanced post predict(xb)

This has produced margins which demonstrate proportional change in the IV which is consumption in the last 30 days (ind14_3e  ) for a proportional change  in package_den (package density outlet) for each value of saq01 (age) 

------------------------------------------------------------------------------
             |            Delta-method
             |      ey/ex   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
package_den  |
         _at |
          1  |   .5296682   .1764275     3.00   0.003     .1838768    .8754597
          2  |   .4362936   .1559193     2.80   0.005     .1306975    .7418898
          3  |   .3027863     .14129     2.14   0.032      .025863    .5797095
          4  |     .09619    .158365     0.61   0.544    -.2141997    .4065797
          5  |  -.2661349   .2630258    -1.01   0.312     -.781656    .2493861
          6  |  -1.066769   .6232084    -1.71   0.087    -2.288235    .1546972
------------------------------------------------------------------------------


However, a reviewer has stated the following: "When interpreting the results, it would be better to specify the ORs rather than just writing the signs (e.g., how much percentage of alcohol consumption was increased)".

I am not sure whether the reviewers suggestion is valid.  I do not think you can convert the elasticity into an odds ratio. 

Is anyone able to shed some insight into what is being asked and how it can be done?


Thank you in advance


Bosco Rowland, PhD, MAPS
Alfred Deakin Post Doctoral Research Fellow
School of Psychology, Faculty of Health


Deakin University
Melbourne Burwood Campus, 221 Burwood Highway, Burwood, VIC 3125
+61 3 92443002
bosco.rowland@deakin.edu.au
www.deakin.edu.au
Deakin University CRICOS Provider Code 00113B




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