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


From   nagi kayo <kayonagi@hotmail.co.jp>
To   statalist質問用 <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Re: st: Re: st: BOOTSTRAP: the standard errors of marginal effects of MIXLOGIT‏
Date   Fri, 22 Jun 2012 17:58:49 +0900

Dear Professor Hole

 

Thank you very much for your prompt reply and for your detailed advice again.

I will consider your advice deeply and try to fix this problem myself.

I greatly appreiate your help.

 

with best wishes,

Kayo



----------------------------------------
> Date: Fri, 22 Jun 2012 09:48:21 +0100
> Subject: st: Re: st: Re: st: BOOTSTRAP: the standard errors of marginal effects of MIXLOGIT‏
> From: arnehole@gmail.com
> To: statalist@hsphsun2.harvard.edu
>
> Dear Kayo,
>
> You need to set this up similarly to the example in the tread I
> referred to. Note in particular the following:
>
> 1) You need to tell -bootstrap- to draw groups of observations
> relating to individuals with replacement, not single observations.
> This is done using the -cluster()- option of bootstrap.
>
> 2) You need to ensure that proper ID variables are used when running
> -mixlogit- on the bootstrap samples. This is done using the
> -idcluster()- and -group()- options of bootstrap.
>
> If you read "help bootstrap" carefully so that you understand the
> meaning of these options and look at the example that I sent you again
> I think you'll be able to fix the problem yourself.
>
> Arne
>
> On 22 June 2012 08:34, nagi kayo <kayonagi@hotmail.co.jp> wrote:
> >
> > 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:
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> >> * http://www.stata.com/support/statalist/faq
> >> * http://www.ats.ucla.edu/stat/stata/
> >
> > *
> > * For searches and help try:
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> > * http://www.ats.ucla.edu/stat/stata/
>
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