Good afternoon, everyone.
Recently I work on my research about the impact of membership of free trade agreement on Indonesia's export performance. I use panel data, consist of 35 Indonesia's main export destination countries (9 of them are ASEAN's member and the rest are not). This research is using export demand approach so the model will be like this:
ln exportijt = b0 + b1 ln gdprealjt + b2 realexchangeratejt + b3 cpiratioijt + b4 AFTA + eijt
i denotes for Indonesia (exporter)
j denotes for export destination countries (9 ASEAN countries and 26 non-ASEAN countries, or so called as importer in this case)
t denotes for year
Variable AFTA is a dichotomy (dummy) variable which takes value of 1 if the export destination countries is member of AFTA (ASEAN Free Trade Area) and 0 otherwise. Thus, I create dummy variable with this command:
gen AFTA=0
replace AFTA=1 if (importer=="BRN")
replace AFTA=1 if (importer=="KHM")
replace AFTA=1 if (importer=="LAO")
replace AFTA=1 if (importer=="MYS")
replace AFTA=1 if (importer=="MMR")
replace AFTA=1 if (importer=="PHL")
replace AFTA=1 if (importer=="SGP")
replace AFTA=1 if (importer=="THA")
replace AFTA=1 if (importer=="VNM")
Here I have 3 questions:
1. When I run regression with Fixed Effect command (xtreg lnexport lnrealGDP lnrealexchangerate lncpiratio AFTA, fe), the AFTA dummy variable is omitted of collinearity. Is there any solution to solve that problem?
2. When estimates about trade performance in panel data, I found some literature that use country-pair fixed effect and time fixed effect. Is that necessary? Because in that literature I read, they did not explain why they use that. If so, how to interpretate those effect?
3. Based on the nature of the data I used and the purpose of the research, is Fixed Effect the best method? Because, again, in the literature I read, they always use Fixed Effect Model to estimate the structural model.
Thank you for your advance.
Sincerely,
Alifan Darul
Recently I work on my research about the impact of membership of free trade agreement on Indonesia's export performance. I use panel data, consist of 35 Indonesia's main export destination countries (9 of them are ASEAN's member and the rest are not). This research is using export demand approach so the model will be like this:
ln exportijt = b0 + b1 ln gdprealjt + b2 realexchangeratejt + b3 cpiratioijt + b4 AFTA + eijt
i denotes for Indonesia (exporter)
j denotes for export destination countries (9 ASEAN countries and 26 non-ASEAN countries, or so called as importer in this case)
t denotes for year
Variable AFTA is a dichotomy (dummy) variable which takes value of 1 if the export destination countries is member of AFTA (ASEAN Free Trade Area) and 0 otherwise. Thus, I create dummy variable with this command:
gen AFTA=0
replace AFTA=1 if (importer=="BRN")
replace AFTA=1 if (importer=="KHM")
replace AFTA=1 if (importer=="LAO")
replace AFTA=1 if (importer=="MYS")
replace AFTA=1 if (importer=="MMR")
replace AFTA=1 if (importer=="PHL")
replace AFTA=1 if (importer=="SGP")
replace AFTA=1 if (importer=="THA")
replace AFTA=1 if (importer=="VNM")
Here I have 3 questions:
1. When I run regression with Fixed Effect command (xtreg lnexport lnrealGDP lnrealexchangerate lncpiratio AFTA, fe), the AFTA dummy variable is omitted of collinearity. Is there any solution to solve that problem?
2. When estimates about trade performance in panel data, I found some literature that use country-pair fixed effect and time fixed effect. Is that necessary? Because in that literature I read, they did not explain why they use that. If so, how to interpretate those effect?
3. Based on the nature of the data I used and the purpose of the research, is Fixed Effect the best method? Because, again, in the literature I read, they always use Fixed Effect Model to estimate the structural model.
Thank you for your advance.
Sincerely,
Alifan Darul
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