What model to employ for panel data including both time-constant and non-stable variables

Hi everyone,

I am currently researching in the field of foreign aid, investigating the effect of different criteria on foreign aid levels. My dataset is covering 21 years of information on annual foreign aid levels (fluctuating dependent variable) as well as a variety of independent variables such as a shared official language of donor and recipient country (time-constant binary type) a former colonial relationship between donor and recipient (time-constant binary type) but also "fluctuating" observations on numerical input variables such as annual trade volume between both parties or the size of a potential diaspora of the recipient country in the donor country.

I bring some basic knowledge of statistics and R but it has been some time since I last worked in the respective field. Considering both my variation in terms of input-type of variables (time-constant vs fluctuating over time) as well as the more general time effects, I was wondering what would be the most suitable model in R to investigate on the effect of the respective variables. As far as I am concerned, employing a classical Fixed-Effects model would not work given the two time-constant variables. I am therefore currently thinking of a Distributed Lag, Random-Effects model to account for both types of variables as well as the very likely lag in effect of the IVs. Where are the downsides / issues I am currently not recognizing?

I would be very thankful for any further ideas how to proceed with my analysis here as well as any potential criticism!

This topic was automatically closed 21 days after the last reply. New replies are no longer allowed.

If you have a query related to it or one of the replies, start a new topic and refer back with a link.