hi all,
after searching statalist and the web, i concluded
that fractileplot is the only Stata machinery readily
available for performing multivariate, nonparametric,
kernel-based smoothing (curve fitting). so my many
thanks to the author of this extremely useful tool,
and if anyone is aware of any other relevant Stata
tools please advise.
my question pertains to how to best use fractileplot
to capture possible interactive effects among the
right hand side variables. from reading the help file
and going through the ado file, it looks to me like
fractileplot assumes that the effect of each rhs
variable is strictly additive. please correct me if
that is not accurate.
i am trying to use fractileplot while allowing for
interaction effects, and i would welcome comments and
suggestions as to my proposed back-of-the-envelope
recipe below. for simplicity, lets consider the
bivariate case, i.e., what would normally by estimated
as "fractileplot y x1 x2".
to capture interactive effects, i suggest doing the
following:
. egen std_x1 = std(x1)
. egen std_x2 = std(x2)
. gen x12 = std_x1 * std_x2
. fractileplot x1 x2 x12
i dont have a well articulated explanation for why i
standardized x1 and x2 prior to the creation of the
interactive variable, just a vague notion that this
would fit best with the whole concept of
distributional smoothing.
some basic questions and concerns that i have around
this include:
- is an interactive term a reasonable approach to
expanding beyond additive marginals?
- if so, does the standardization of x1 and x2 prior
to their interaction make sense or does it carry
hidden dangers?
- any other suggestions for how to allow for
interactive effects?
- any other easy recipes for multivariate
beyond-additive nonparametric smoothing using existing
Stata commands and ado's?
thanks in advance for your time and please accept my
apologies for any abuse of terminology (and science).
PSX
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