Dear All,
I am currently working on my dissertation about the effect of environmental policy stringency index on Turkey`s inward FDI stocks. For the study, I employed panel data of OECD and BRIICS countries from 2009-2020. I utilised PPML for zero values in my dataset, and I am using the code ppmlhdfe in Stata. I am new to this estimation technique and having some problems. The problem that I am encountering is that my fixed effects are perfectly multicollinear with some of my variables. For instance, when I employ time-fixed effects, Turkey`s GDP, POP, HDI, infrastructure, inflation and institutional quality variables are omitted from the model. In addition, when I control for country-fixed effects, the religion proximity index and distance variables are omitted. I am trying to find a way to both control fixed effects and have the relevant variables in my model.
1) Can some things be improved about how I constructed the data? Is it normal to encounter such a problem?
2) One of my ideas was to construct variables in relative terms for GDP, POP, HDI, infrastructure, inflation and institutional quality. Is it a good strategy? What can be done for religion proximity and distance variables? I would appreciate it if you could share your ideas with me.
Also, unfortunately, my sample size is small.
Dear Joao Santos Silva, I would appreciate it a lot if you could help me with this.
I am currently working on my dissertation about the effect of environmental policy stringency index on Turkey`s inward FDI stocks. For the study, I employed panel data of OECD and BRIICS countries from 2009-2020. I utilised PPML for zero values in my dataset, and I am using the code ppmlhdfe in Stata. I am new to this estimation technique and having some problems. The problem that I am encountering is that my fixed effects are perfectly multicollinear with some of my variables. For instance, when I employ time-fixed effects, Turkey`s GDP, POP, HDI, infrastructure, inflation and institutional quality variables are omitted from the model. In addition, when I control for country-fixed effects, the religion proximity index and distance variables are omitted. I am trying to find a way to both control fixed effects and have the relevant variables in my model.
1) Can some things be improved about how I constructed the data? Is it normal to encounter such a problem?
2) One of my ideas was to construct variables in relative terms for GDP, POP, HDI, infrastructure, inflation and institutional quality. Is it a good strategy? What can be done for religion proximity and distance variables? I would appreciate it if you could share your ideas with me.
Also, unfortunately, my sample size is small.
Dear Joao Santos Silva, I would appreciate it a lot if you could help me with this.
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