How to determine correct fixed effects in linear regression model using unbalanced paneldata I am currently working on a research project regarding the introduction of mandatory earnings guidance regulation in multiple countries within a time span of 10 years (staggered adoption). As I can see that my results depend on the FE I use, I tried to figure out how to determine which FE I have to use in this setting. Literature doesn't give a clear answer as different authors use different FE in similar models (firm, year, country, industry or a combination of those four). That's why I wanted to ask the community: Is there any rule-of-thumb or a precise answer on which FE I do have to include in my multi-year multi-country model (using linear model with an unbalanced dataset) in the form of: 


  Market liquidity measure = b0 + Analyst_following * x1 + US_Listing * x2 + IFRS_User * x3 + MarketCapitalization * x4 + ShareVolatility * x6 + e


I have detailed firm level data for all variables. I would suggest industry-year FE, but don't have any arguments why I should use them (just intuition). Is there any clear guidance on which FE to use or any recommendation for a book adressing this topic in an inuitive way? Most books deal with social research questions (using e.g. race as FE as it does not change) but it is hard for me to transfer this logic to a business regulation setting...
What I want to measure is, whether the staggered adoption of the regulation has a significant impact on market/stock liquidity. Dependant/independant variables are derived from well-established papers in my field.

Any help appreciated!
It seems that you have firm-level panel data over multiple industries in multiple countries.

Year fixed effects control for trends. Do not omit them. For others, a general rule is: ___ FE's control for time-invariant heterogeneity across different ____, but do not control for heterogeneity within the same ____. You replace ____ with "firm", "industry", "country", "industry-country", etc.

Firm FE's successfully control for all firm-level time-invariant effects that are possibly correlated with the RHS variables. So you compare different years within the same firm. In contrast, industry FE's control for time-invariant effects (that are possibly correlated with the RHS variables) across different industries, but do not control for firm-level heterogeneity (fixed effects) within the same industry. And so on. 

I personally think a model with firm FE (and year effects) is easiest to defend using. If you can explain why firms within the same industry are homogenous, you can use industry FE, etc.