Fixed Effects Regression Constant / Intercept Using LFE (FELM) in R When calculating a panel data regression with multiple fixed effects using the felm() (of the lfe package), no constant / intercept is generated in the summary results.
Why would there be no constant generated?
When using Stata (xtreg, fe), for example, an intercept is generated by default. My understanding is that the "constant" is not a real constant, but rather a grand mean of all constants. Is that correct?
(I apologize in advance for not providing a reproducible example...)
Here is an excerpt from Simen Gaure's article in The R Journal (Vol. 5/2, December 2013) https://journal.r-project.org/archive/2013/RJ-2013-031/RJ-2013-031.pdf
"The careful reader has noticed that the behaviour of summary() on a ’felm’ object with respect to degrees of freedom and R2 is the same as that of on an ’lm’ object when including an intercept. There is no explicit intercept in the result of felm(), but the factor structure includes one implicitly."
Thank you!
 A: Any regression package will either (1) include an intercept and drop one dummy to avoid collinearity or (2) not include an intercept but not drop any dummies. In case (1) the interpretation of any dummy coefficient is "in respect to the average of all others" and in specification (2) it is in respect to the one omitted where the intercept is shared for all. 
A: As I read the vignette of this package (which is not being actively maintained and is at risk of removal from CRAN) the goal of the "demeaning" procedure is not to support estimation of overall effects, but is rather to substitute via projection the means of factor levels for the factors themselves. As I read the vignette this is not done in a manner that accounts for their numbers to support a weighting that allows population predictions. This is done for the factors thought to be "nuisance" factors and no estimates are needed.
I'm not convinced that the Stata xtreg, fe function are directly comparable to felm. I think you should be looking at the "standard" mixed model functions in the 'lme4' package as well as the function in pkg:plm (for the panel regression that xtreg offers). (There is a mixed models mailing list where you would undoubtedly get a better answer than I can provide.)
