How to include covariates in a nested model? I am new to multilevel model and having trouble understanding how to include covariates. In my mode, I have Industry and Country as two factors. I have to control for the effects of following two covariates to determine the effect of my focal variable X on Y: IndustryProfitability and CountryRiskRatings.  Which of the following would you think is the most appropriate model specification for this purpose?
Model 1 = lmer(Y ~ X + IndustryProfitability + CountryRiskRatings, myData)

Model 2 = lmer(Y ~ X + (1|Industry)+ (1|Country), myData)   

Model 3 = lmer(Y ~ X + (1|Industry)+ (1|Country) + IndustryProfitability + CountryRiskRatings, myData)

I was told that Model 3 is the most appropriate as there is non-independence in my data due to industry and country. But I am confused as the two covariates (e.g., IndustryProfitability) relating to a higher level (e.g., Industry) are included as fixed effects in the Model 3. Are Model 1 and Model 2 better or misspecified?       
 A: >  Model 1 = lmer(Y ~ X + IndustryProfitability + CountryRiskRatings, myData)

This is not a mixed effects model and will not run. It will return an error:

Error: No random effects terms specified in formula

Then we have:  
>  Model 2 = lmer(Y ~ X + (1|Industry)+ (1|Country), myData)   

This will fit a linear mixed effects model and will, subject to convergence, estimate the fixed effect of X while accounting for the non-independence of observations within the clusters. However, it will obviously not be adjusted for IndustryProfitability or CountryRiskRatings which is the point of the OP.
So we can disrgard models 1 and 2.
This leaves:  
>  Model 3 = lmer(Y ~ X + (1|Industry)+ (1|Country) + IndustryProfitability + CountryRiskRatings, myData)

This indeed will adjust for the 2 covariates. The fact that they vary at the cluster level does not matter - they should be automatically be handled at the correct level. However, I advise a little caution here. If the 2 covariates are a cause, or a proxy of a cause, of both X and Y, and neither are a cause of the other, then they are potential confounders and should be adjusted for. However, if either of them are on the causal pathway from X to Y, then they are mediators and should not be adjusted for if your model is to be used for inference - if it is a predictive model only then this is not so important. 
