Thank you,
because I am here, and if you do not get tired on me (I think it should be).
I asked about the marginal effects, because I want to compare my
estimates in level with those in difference: i.e my estimates in level
are in log-level and those in difference are in difference-log-level.
I know that if the hypothesis of correlation is correct the two
estimartes have to be different, but my diff gmm (in log) estimates
are like ten times those in log level...
does not it appear strange to you??
Thanks for your other answers
alessia
2007/5/22, Kit Baum <baum@bc.edu>:
sacrificial radish
Alessia said
I try to explain it better then. I didi my regressione, kept my b. Now
there is a x variable in which I did a variation (I took one of its
observation and I modify it, saying doing a 1% variation. I want then
to know how the predicted values are going to change).
regress y x1 x2
mfx compute, dyex
will compute the effect on the level of y of a 1% change in each x.
Re other q's:
1) you can always compute an R^2-like measure from the squared simple
correlation of Y and Yhat.
2) estimating a model with difference GMM is an instrumental
variables estimator. One would expect any IV estimator to give you
different results than would OLS on levels if your maintained
hypothesis in running difference GMM -- that some of the explanatory
variables are correlated with the error -- is appropriate.
3) See the Baum-Schaffer-Stillman paper (Stata Journal, 2003) or the
working paper version available from my RePEc CV below. If you're
going to use xtabond2, read David Roodman's "how to do
xtabond2" (available from IDEAS or EconPapers).
Kit Baum, Boston College Economics and DIW Berlin
http://ideas.repec.org/e/pba1.html
An Introduction to Modern Econometrics Using Stata:
http://www.stata-press.com/books/imeus.html
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