Michael Blasnik replied to Yvonne Capstick:
> The -egen group- suggestion will provide for fully interacted effects, but
> I think that just the main effects are wanted (unless I am guessing wrong
> about the somewhat hazy intention). The only way I know of doing this is
> to allow areg or xtref, fe to absorb one of the variables and to add
> dummies for the other (e.g., using xi).
Like Michael, I too interpreted Yvonne to want two seperate fixed effects
inserted into the -i()- option for her -xtreg, fe- model, which you can't
have. Austin Nichols' rather neat suggestion
> . egen g=group(state ind)
> . areg y x, absorb(g)
does what I should have figured out: generate a single fixed effect
variable combining both sets of information. But isn't there a problem
with this: namely, the codes for this (in effect) interaction term are not
readily interpretable?
Say we have a dataset of ten states and ten industry sectors, both coded
from 1 to 10. We then run Austin's code and get our FE variable (let's
call it G) that Yvonne thinks she's got. This will give her codes running
from 1 to 100. The question is, is Yvonne comfortable with trying to
interpret lower numbers of G as representing certain industries (or
states) and higher numbers of G as representing other industries (or
states)? Since these categories that comprise G are _nominal_, rather than
ordinal, I wouldn't be comfortable with this at all.
Perhaps you can acheive this if your number of categories for each of the
variables is very small, but if not, I think you'd be easily lost in a
large mire of output codes. I know I would be. If you can figure out a way
around it, then you're a smarter bod than I.
I should also make a correction here: when I discussed time dummies in my
reply to Yvonne, I, of course, meant state dummies (which Yvonne wants).
Apologies there. Anyway, the point I was making still stands.
CLIVE NICHOLAS |t: 0(044)7903 397793
Politics |e: [email protected]
Newcastle University |http://www.ncl.ac.uk/geps
Whereever you go and whatever you do, just remember this. No matter how
many like you, admire you, love you or adore you, the number of people
turning up to your funeral will be largely determined by local weather
conditions.
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