I have observational data that was collected within the same year but on different days and in different European countries. To analyse the causality of a certain event (outcome) on the dependent variable I want to run a regression.

Additionally to the treatment variable I also want to control for individual characteristics but also country-dependent effects (e.g. previous events that might have happened such as election) that might bias the coefficient of the event of interest.

Now, my questions to this attempt:

1. For the individual characteristics I create a macro containing the single variables. I add the macro to the regression just as the treatment variable. For the country-dependent effect: should I just add a macro with the countries to the regression or should I use clustering (vce cluster or absorb). Is there any difference between these two approaches?
2. If individual effects are added to the regression as described in 1., do you still have to add a ",fe"-command to the regression code?
3. Online I have seen different explanations about panel models. Mostly it says that panel data is a combination of cross-sectional and time-series Dara. Since my data was collected in a single year but on different days and in different countries, is this still seen as panel data (different countries, days difference in data collection)? And how do you best control for the days difference in data collection? I was thinking about controlling for weekdays, and month of the year. Would this be a good solution?