Your R^2 should be much higher in this case (you were right to flag that as a concern. Now that your regression includes all these dummies separately, it will have many more covariates and will absorb much more information. Ppml x log_distw log_pop_d log_pop_o log_gdp_d log_gdp_o contig comlang_off col45 gatt_o gatt_d fta_wto _Iexp_TIFE* _IimpTIFE* _ITFE* Try the following:Īnd notice how this creates three sets of dummy variables corresponding to exporter, importer, and year (open up your data window and browse through them.) These dummies should be respectively indexed as _Iexp_TIFE*, _IimpTIFE* and _ITFE* Now estimate the following: However, that said, I suggest first familiarizing yourself with how to estimate the fixed effects the hard way. Ppml x log_distw log_pop_d log_pop_o log_gdp_d log_gdp_o contig comlang_off col45 gatt_o gatt_d fta_wto, absorb(exp_TIFE imp_TIFE TFE)Īnd this will specify the fixed effects properly for you. Assuming exp_TIFE, imp_TIFE, TFE are coded numerically, you can type It has some some variables like Trade (X) Distance (distw) Population (POP) Gross Domestic Product (GDP) Contiguity (contig) Common language (comlangoff) Colony (col45) GATT member (GATT) Free trade Agreement. I am trying to run a gravity model with ppml for 133 countries from 2005 to 2018. With ppmlhdfe, it is fortunately easy to emulate these dummies. Firstly I would like to say I am a new Stata User. You have each of them as a single variable but technically you should have a dummy variable for each fixed effect category (i.e., three sets of dummies, with unique dummies for each exporter, importer, and year). It looks like the specification of the fixed effects is not being coded right.
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