RE: st: Re: st: BOOTSTRAP: the standard errors of marginal effects of MIXLOGIT‏

[nagi kayo <kayonagi@hotmail.co.jp>]

Dear Professor Hole

 

Thank you very much for your prompt reply. I greatly appreciate your help, 

and I am realy sorry for my delay in responding.

 

> Your procedure is incorrect as the model needs to be re-estimated for
> each bootstrap sample. In other words your “marginal_inc” program
> should include the call to -mixlogit-. 

 

Based on your advice, I corrected my program as follows:

 

cap program drop marginal_inc
program marginal_inc, rclass
  mixlogit d d1 d2 d3 d1inc d2inc d3inc, group(id) rand(p)
  mixlpred prep_base
  quietly replace d1inc=d1inc+1 if alt==1
  quietly replace d2inc=d2inc+1 if alt==2
  quietly replace d3inc=d3inc+1 if alt==3
  quietly replace d4inc=d4inc+1 if alt==4
  mixlpred prep_inc

  quietly sum prep_base if alt==1
  local av_prep_base1 = r(mean)
  quietly sum prep_inc if alt==1
  local av_prep_inc1 = r(mean)
  return scalar marge_inc1 = `av_prep_inc1' - `av_prep_base1'
  quietly sum prep_base if alt==2
  local av_prep_base2 = r(mean)
  quietly sum prep_inc if alt==2
  local av_prep_inc2 = r(mean)
  return scalar marge_inc2 = `av_prep_inc2' - `av_prep_base2'
  quietly sum prep_base if alt==3
  local av_prep_base3 = r(mean)
  quietly sum prep_inc if alt==3
  local av_prep_inc3 = r(mean)
  return scalar marge_inc3 = `av_prep_inc3' - `av_prep_base3'
  quietly sum prep_base if alt==4
  local av_prep_base4 = r(mean)
  quietly sum prep_inc if alt==4
  local av_prep_inc4 = r(mean)
  return scalar marge_inc4 = `av_prep_inc4' - `av_prep_base4'
end
bootstrap r(marge_inc1), reps(1000): marginal_inc
bootstrap r(marge_inc2), reps(1000): marginal_inc
bootstrap r(marge_inc3), reps(1000): marginal_inc
bootstrap r(marge_inc4), reps(1000): marginal_inc


However, STATA returned the follwing error message.

 

insufficient observations to compute bootstrap standard errors
no results will be saved
r(2000);


Could you please teach me which part of my program is incorrect?

I am really sorry to trouble you so much.

 

with my thanks and best wishes,

Kayo

 

 

 


----------------------------------------
> Date: Sun, 3 Jun 2012 13:09:57 +0100
> Subject: st: Re: st: BOOTSTRAP: the standard errors of marginal effects of MIXLOGIT‏
> From: arnehole@gmail.com
> To: statalist@hsphsun2.harvard.edu
>
> Dear Kayo
>
> Your procedure is incorrect as the model needs to be re-estimated for
> each bootstrap sample. In other words your “marginal_inc” program
> should include the call to -mixlogit-. Whether this is practical or
> not depends on how long it takes to estimate your model – you may be
> in for a long wait!
>
> See this thread
> <http://www.stata.com/statalist/archive/2010-11/msg01025.html> for an
> example of how -bootstrap- can be used with -mixlogit-.
>
> Arne
>
> PS Note that you can bootstrap several statistics in one go – you
> don’t need to run -bootstrap- for each marginal effect.
>
> On 3 June 2012 09:01, nagi kayo <kayonagi@hotmail.co.jp> wrote:
> > Dear Professor Arne Risa Hole and all
> >
> > I estimated mixed logit model using the command -mixlogit- and
> > calculated the marginal effects using the command -mixlpred-.
> > (Once again, I greatly appreciate that professor Arne Risa Holl
> >
> > gave me advice on how to calculate the marginal effects using
> >
> > "mixlpred" last february)
> >
> > However, now I am having trouble obtaining the standard errors and
> > p-value of marginal effects using the bootstrap.
> >
> > First, please let me explain the data.
> > My data set is like below.
> > (Actually, my data set inlude 10,000 id, but here I show you the data only on two id
> > for simplicity. In addition, the data I used in the estimation include
> >
> > the data on many household characteristics such as age, wealth, education level.)
> >
> > id alt d d1 d2 d3 d1inc d2inc d3inc d4inc p
> > 1 1 1 1 0 0 665 0 0 0 0.214
> > 1 2 0 0 1 0 0 665 0 0 0.186
> > 1 3 0 0 0 1 0 0 665 0 0.381
> > 1 4 0 0 0 0 0 0 0 665 0.219
> > 2 1 0 1 0 0 779 0 0 0 0.553
> > 2 2 1 0 1 0 0 779 0 0 0.301
> > 2 3 0 0 0 1 0 0 779 0 0.107
> > 2 4 0 0 0 0 0 0 0 779 0.039
> >
> >
> >
> > id: households
> > alt: 1=households are worried about their retirement life
> > because the pension benefit is NOT enough.
> > 2=households are worried about their retirement life
> > for some reasons other than pension.
> > 3=households are NOT worried about their retirement life
> > because the pension benefit is enough.
> > 4=households are NOT worried about their retirement life
> > for some reasons other than pension.
> > d: dummy which equals one if households choose "alt" in the same row.
> > (so id 1 chose alt 1, and id 2 chose alt 2.)
> > d1-d3: intercepts
> > d1inc-d4inc: real households income
> > p: an alternative specific variable
> >
> >
> >
> > using the above data, i did the estimation as follows:
> >
> >
> >
> > **********************************************************************
> > mixlogit d d1 d2 d3 d1inc d2inc d3inc, group(id) rand(p)
> > mixlpred prep_base
> > preserve
> > quietly replace d1inc=d1inc+1 if alt==1
> > quietly replace d2inc=d2inc+1 if alt==2
> > quietly replace d3inc=d3inc+1 if alt==3
> > quietly replace d4inc=d4inc+1 if alt==4
> > mixlpred prep_inc
> > cap program drop marginal_inc
> > program marginal_inc, rclass
> > quietly sum prep_base if alt==1
> > local av_prep_base1 = r(mean)
> > quietly sum prep_inc if alt==1
> > local av_prep_inc1 = r(mean)
> > return scalar marge_inc1 = `av_prep_inc1' - `av_prep_base1'
> > quietly sum prep_base if alt==2
> > local av_prep_base2 = r(mean)
> > quietly sum prep_inc if alt==2
> > local av_prep_inc2 = r(mean)
> > return scalar marge_inc2 = `av_prep_inc2' - `av_prep_base2'
> > quietly sum prep_base if alt==3
> > local av_prep_base3 = r(mean)
> > quietly sum prep_inc if alt==3
> > local av_prep_inc3 = r(mean)
> > return scalar marge_inc3 = `av_prep_inc3' - `av_prep_base3'
> > quietly sum prep_base if alt==4
> > local av_prep_base4 = r(mean)
> > quietly sum prep_inc if alt==4
> > local av_prep_inc4 = r(mean)
> > return scalar marge_inc4 = `av_prep_inc4' - `av_prep_base4'
> > end
> > bootstrap r(marge_inc1), reps(1000): marginal_inc
> > bootstrap r(marge_inc2), reps(1000): marginal_inc
> > bootstrap r(marge_inc3), reps(1000): marginal_inc
> > bootstrap r(marge_inc4), reps(1000): marginal_inc
> > restore
> > ************************************************************************
> >
> >
> > The results of bootstrap is as follows:
> >
> > Standard errors P-value
> > marge_inc1 -0.0011 4.44e-06 0.000
> > marge_inc2 -0.0008 2.70e-06 0.000
> > marge_inc3 0.0013 5.41e-06 0.000
> > marge_inc4 0.0007 3.36e-06 0.000
> > ************************************************************************
> >
> >
> > Apparently, there is no problem in the above results.
> > However, all the standard errors of marginal effects of ANY explanatory variable
> > (e.g. age, education level, risk aversion) are very small,
> > and all p-values of marginal effects of ANY explanatory variables are 0.000.
> > I am wondering if my program has something wrong.
> >
> > Could anyone give me any comments?
> > I greatly appreciate it.
> >
> > with best wishes,
> > KAYO
> >
> > *
> > * For searches and help try:
> > * http://www.stata.com/help.cgi?search
> > * http://www.stata.com/support/statalist/faq
> > * http://www.ats.ucla.edu/stat/stata/
>
> *
> * For searches and help try:
> * http://www.stata.com/help.cgi?search
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/ 		 	   		  

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