Dear all,
This thread is partially related to comment #6 here: https://www.statalist.org/forums/for...ative-binomial and to this thread: https://www.stata.com/statalist/arch.../msg00889.html. I am using the command ppmlhdfe from ssc install by Sergio Correia.
Suppose I have three variables, Y, X1 and X2. I am actually interested in the interaction between X1 and X2. Y is a non-negative count variable, prompting the use of Poisson. My research question is the following: if X2 rises by 1, how does that affect the relationship between Y and X1? X2 is therefore a so-called moderating variable. Ideally, I would like to know the marginal effect of X1 on Y at different levels of X2.
I also have a three fixed effects vectors: survey respondent-by-season, canton-by-month, sector-by-month. Survey-by-season fixed effects are the only incidental parameters, which grow with sample size; the others are fixed.
The code looks like this:
In comment #6 in the abovementioned thread, Joao Santos Silva very helpfully points out that in nonlinear models, presumably due to the incidental parameters problem, marginal effects are meaningless.
In conditional non-linear model, such as conditional logit, the fixed effects are conditioned out by a sufficient statistic, and are therefore not estimated. Marginal effects can therefore not be computed.
My understanding of ppmlhdfe is that fixed effects included in absorb() are profiled out using the Frisch Waugh theorem, and that estimation is carried out using the residuals. Fixed effects are therefore not estimated per se.
Do the marginal effects still directly depend on the values of the profiled-out fixed effects? In this context, would the margins command give any meaningful results?
If not, will the Poisson coefficient on the interaction term yield the effect that I am interested in?
This thread is partially related to comment #6 here: https://www.statalist.org/forums/for...ative-binomial and to this thread: https://www.stata.com/statalist/arch.../msg00889.html. I am using the command ppmlhdfe from ssc install by Sergio Correia.
Suppose I have three variables, Y, X1 and X2. I am actually interested in the interaction between X1 and X2. Y is a non-negative count variable, prompting the use of Poisson. My research question is the following: if X2 rises by 1, how does that affect the relationship between Y and X1? X2 is therefore a so-called moderating variable. Ideally, I would like to know the marginal effect of X1 on Y at different levels of X2.
I also have a three fixed effects vectors: survey respondent-by-season, canton-by-month, sector-by-month. Survey-by-season fixed effects are the only incidental parameters, which grow with sample size; the others are fixed.
The code looks like this:
Code:
ppmlhdfe y c.x1##c.x2, sep(fe ir) abs(i.respondent#i.year) cluster(respondent)
In conditional non-linear model, such as conditional logit, the fixed effects are conditioned out by a sufficient statistic, and are therefore not estimated. Marginal effects can therefore not be computed.
My understanding of ppmlhdfe is that fixed effects included in absorb() are profiled out using the Frisch Waugh theorem, and that estimation is carried out using the residuals. Fixed effects are therefore not estimated per se.
Do the marginal effects still directly depend on the values of the profiled-out fixed effects? In this context, would the margins command give any meaningful results?
If not, will the Poisson coefficient on the interaction term yield the effect that I am interested in?
Comment