Panel Data and model specification

#1
Sorry to ask questions which may be obvious to many, but I am just starting out with Stata and econometrics so if anyone could help me I would really appreciate it.

Basically I need to look at the relationship between inequality and technological change, and am using panel data consisting of a measure of technological change (technology as a % of GDP) and gini index statistics for a sample of countries annually over a 30 year period.

I am only interested in seeing if technological change affects inequality so technology is my only dependent variable. When using xtreg to run a fixed effects regression my Prob>F value is high so is it fair to say that I cant reject the hypothesis that the coefficient is 0 and therefore it is likely that technological change does not affect growth?

Is it ok that I have not included other variables in the regression? I am not really concerned in finding all the factors that influence inequality, only in seeing if technology plays any role. So is my regression ok as it is?

Also, can anyone give me any idea of whether I should be using logged values for my data? I see this done a lot but am unsure when it should be used.

Sorry again for the 'newbie' questions, and thank you for any help!
 
#2
Is it ok that I have not included other variables in the regression? I am not really concerned in finding all the factors that influence inequality, only in seeing if technology plays any role. So is my regression ok as it is?

Also, can anyone give me any idea of whether I should be using logged values for my data? I see this done a lot but am unsure when it should be used.
yes you need other variables in order to exclude the possibility that the inequality is caused by something other than technology. if those other variables are correlated with technology, your results will be biased. please read more about omitted variable bias. this or other forms of biases may affect the significance you get for technology and therefore need to be taken care of before any conclusions can be made.

regression analyses a linear relationship between independent and dependent variables. if there is a reason to believe that the relationship is non-linear, transformations like log are used e.g. if technology change results in greater inequality initially but has no effect after a point, log(technology) will produce a better model.