No idea why you emphysize this problem in EViews. As far as I know, heterogeneity analysis is to supplement the main estimation in emperical reswearch work. Normally we introduce a third key variable to cut the sample into two or three subsamples and see if there are changes about main conclusions.
Heterogenity vs homogenity, these are test of assumption of estimation. That homogenity must fulfilled as a assumption. So, if heterogenity exist in panel data, it means not suitable estimation
Random and fixed effect models capture heterogeneity. @Johnny Walker Situmorang
Thats the difference things. Random and Fixed to choose model estimation due to panel data. While heterogeneity is the test of assumption due to estimation used panel data. It should be homogeneity not heterogeneity.
I think R software is better and there are many packages (ex. lme4) linear mixed effect models. You can try. I think it is very useful.
You need to check coefficients of those variables to show random effect. Those variables show trend of change over time and a variable of random effect. When you use a lme4 package in R you can identify the random effect and the fixed effect at the same time. And then your estimated model will show better fit.
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