New subject: Questions Concerning CLARIFY (Seed and Interaction Terms)

5 Aug 2002

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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RE: Questions Concerning CLARIFY (Seed and Interaction Terms)