Below...
On Mon, 5 Aug 2002, George Krause wrote:
> Gary:
>
> (1) Can one use the standard set seed command performed when
> doing bootstrapping and MC simulations in STATA before using
> estsimp command of CLARIFY so that one can reproduce the
> same estimates in subsequent runs of a simulation on the same model?
I think so, but I haven't tried it. You can tho. Do set seed, run
clarify and look at the first (say) 10 simulations of some quantity. Then
do set seed again and run it again. If all the simulations are the same,
then it works (with very high probability). If they differ, then there's
probably something else you can do (which perhaps someone else on this
list I'm CCing can suggest).
>
> (2) Can one account for Robert Friedrichlike (AJPS1982)
> conditional marginal effects in CLARIFY? If so, how can this be done
> using CLARIFY within STATA? From examining the CLARIFY documentation on
> the web, it seems that to me that interaction terms can only be analyzed
> in isolation from the linear term that conditions the formers impact
> on the dependent variable (e.g., the FAQ on interaction terms leads me
> to think this way plus the CLARIFY documentation that I examined -
> though maybe I am overlooking something). In other words, if we have the
> following model:
>
> Turnout = alpha + Beta_1*Education + Beta_2*Race
> + Beta_3*(Education *Race) + e
>
> can we perform CLARIFY analysis on Beta_1 + Beta_3 (or Beta_2 + Beta_3)
> i.e., the conditional (marginal) effect of Beta_3 on Y and its
> corresponding conditional standard errors? Or is it that one can only
> analyze Beta_3 separately from Beta_1 using CLARIFY? If the latter is
> true, then this suggests that analyzing Beta_3 via CLARIFY only provides
> information on the deviation from the baseline effect Beta_1 (i.e.,
> partial effect) and not the conditional (full) effect of Beta_1 +
> Beta_3.
With Clarify, you can decide on the quantity of interest (beta_1 or
beta_1+beta3 or sqrt(beta_1+log(beta_3)) or anything else. then the
procedure is to use clarify to make that calculation for the simulations
so that you wind up with simulations of your quantity of interest. at
that point, you can summarize your M simulations any way you like, such as
with se's or confidence intervals.
Best of luck,
Gary
: Gary King, King(a)Harvard.Edu http://GKing.Harvard.Edu :
: Center for Basic Research Direct (617) 495-2027 :
: in the Social Sciences Assistant (617) 495-9271 :
: 34 Kirkland Street, Rm. 2 HU-MIT DC (617) 495-4734 :
: Harvard U, Cambridge, MA 02138 eFax (928) 832-7022 :
>
> Any thoughts/advice on these matters is greatly appreciated. Thank
> you for your time and consideration of my queries.
>
> Best Regards,
>
> George Krause
>
> George A. Krause
> Associate Professor of Political Science
> Department of Government and International Studies
> 337 Gambrell Hall
> University of South Carolina
> Columbia, South Carolina 29208
> (803) 777-4545/3109 (office/department phone)
> (803) 777-8255 (fax)
> George.Krause(a)sc.edu (e-mail)
> http://www.cla.sc.edu/GINT/facbio/krause.html (web bio)
>
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