I'm working with research that has cross-sectional data. I have collected information about publicly-listed banks in many countries. For example, for each bank I collected the following information:

I have 619 banks in 58 different countries. In my research, I want to test how variations in bank characteristics (Tier 1, Tangible Equity, etc.) affected bank stock returns during crisis time. My equation is as follows:

BPb,c = 𝑎 + 𝛽1RETURNS_2019b + 𝛽2TIER_1b + 𝛽3DEPb,+ 𝛽4NPLb + 𝛽5NONIIb + 𝛽6LIQASSb + 𝛽8SIZEb + 𝛽9DENb + 𝛽10ROAEb + 𝛽11 LOANSb + 𝛽3*TANEQb + 𝛾c + ub,c

Where BPb,c is the performance of a bank b in country c. The coefficients 𝑎, 𝛽, represent vectors of coefficient estimates and ub.c is the error term. 𝛾c - country fixed effects.

In all the literature I have read, fixed effects are applied to panel data models. However, following Beltrati and Stulz (2012), which to my understanding, has cross-sectional data as well, they apply fixed effects and use standard errors clustered by country.

Is this approach using country-fixed effects and clustering error by country (with cross-sectional data) logical? Also, perhaps someone could advise how to implement this model in Stata.