Dear Statalisters,

I have a cross-sectional data from many industries which I will use to estimate the income elasticity of each industry. The data look something like this:
id lcon1 lcon2 lcon3 lcon4 gdppc 1 ### ### ### ### ### 2 ### ### ### ### ### 3 ### ### ### ### ### 4 ### ### ### ### ### 5 ### ### ### ### ### 6 ### ### ### ### ###
where "id" is the identifier for each country, "lcon1" - "lcon4" are each country's consumption (in log) in industry 1 through 4 and "gdppc" is per-capita income of each country.

Then I would like to simultaneously estimate these following for equations (since each country consumes goods from all 4 industries):

lcon1_i=a1+b1*gdppc+X+e1_i
lcon2_i=a2+b2*gdppc+X+e2_i
lcon3_i=a3+b3*gdppc+X+e3_i
lcon4_i=a4+b4*gdppc+X+e4_i

where "X" are the set of control variables and "e1" - "e4" are error terms. To my understanding, these are not "structural" equations as they are essentially the same equation repeating for different sectors. Then the idea is to estimating the 4 equations simultaneously by minimizing the sum of the squares of the error terms: sum_i (e1_i^2+e2_i^2+e3_i^2+e4_i^2).

Is there a way to achieve this using STATA?

Thanks a lot!