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st: RE: [Non Stata] Estimation strategy for a belief learning model.


From   austin nichols <austinnichols@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   st: RE: [Non Stata] Estimation strategy for a belief learning model.
Date   Mon, 8 Aug 2005 13:12:33 -0400

I don't think you really want a learning curve model (aka
latent growth curve model, see HLM software for these
models), but you do want to allow for round-specific fixed
effects, probably.  Just -tab round, gen(rd)- and then
include rd* as regressors in each model, like so:
   g A=ln(pA/(1-pA))
   reg A theoreticalA rd* , score(sA)
   est store A
   g B=ln(pB/(1-pB))
   reg B theoreticalB rd* , score(sB)
   est store B
   g C=ln(pC/(1-pC))
   reg C theoreticalC rd* , score(sC)
   est store C
   suest A B C, cluster(id)
   test [A]theoreticalA=0
   test [B]theoreticalB=0, accum
   test [C]theoreticalC=0, accum
Maybe you want indiv fixed effects of some kind, too...
  cap drop dummy*
  tab id, gen(dummy)
  foreach v in A B C {
   cap drop `v' 
   cap drop s`v'
   g `v'=ln(p`v'/(1-p`v'))
   reg `v' theoretical`v' rd* dummy*, score(s`v') 
   est store `v'
  }
   suest A B C, cluster(id)
   test [A]theoreticalA=0
   test [B]theoreticalB=0, accum
   test [C]theoreticalC=0, accum

Does any of this help at all?  Maybe, maybe not.

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