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In many cases, I was concerned about which fixed effects I should introduce into the panel data model, especically the data have fine-scaled
temporal resolution, such as daily, monthly, and even hourly. 

My experience is that if the data sample is large, the model tends to return consistent estimates regardless of which fixed effects are included.

But always you need to adjust the fixed effects according to your data and make sure those effects indeed alleviate the omitted variable concern. 

As a result, I decide to document papers I read in which the authors employ detailed data set and introduce carefully designed fixed effects. This endeavor

attempts to provide myself a reference and hopefully could also benefit my peers. 


Another question in my head is what is the best way to explore heterogeneity in a panel data setting?
I have seen regressions on data separated to different groups. Also I see regressions with dummy variables indicating groups. 
Ideally, these two types of models should render similar results.