I'm working on a gravity model of migration for my thesis. My data contains 28 EU countries as destination countries, 130 non-EU countries as origin countries and spans 10 years.
My dependant variable is the number of first-time issued residence permits for employment for any given courtrypair (as I'm focussing on work-related migration) and I want to interpret the influence of different migration policies. Due to the large number of zeros in the dependant variable, I want to use the PPML estimator as described by Santos Silva & Tenreyo (2006) and used in most gravity-related literature in the recent years.
I want to run the following command:
Code:
ppml permits_all lgdp_o lgdp_d ldist contig comlang_off colony comcol market_test shortage_list point_system job_offer dFE_ot*, cluster(dist)
As I'm using Stata 15.1 IC, I can't run the code because 1300 origin*time dummies are too much for the IC version. Before buying a new licence (poor student here), I checked for alternatives and found the following three commands:
1. xtpoisson, fe
2. ppml_panel_sg
3. ppmlhdfe
I'm (theoretically) abel to use xtpoisson, fe and ppml_panel_sg, but since they are set in regards to the kinds of fixed effects they use, they are not practical for me.
I like ppmlhdfe, as I can decide which fixed effects to add (as with ppml), but don't need to inflate my modell with the manually added FE dummy variables (and thus can run the regression in Stata IC).
Now to my question:
Is my understanding correct, that the following to codes would produce the same output?
Code:
ppml permits_all lgdp_o lgdp_d ldist contig comlang_off colony comcol market_test shortage_list point_system job_offer dFE_ot*, cluster(dist)
Code:
ppmlhdfe permits_all lgdp_o lgdp_d ldist contig comlang_off colony comcol market_test shortage_list point_system job_offer, a(i.origin#i.year)
Sarah
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