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From |
Arne Risa Hole <arnehole@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: Re: st: Re: st: BOOTSTRAP: the standard errors of marginal effects of MIXLOGIT |

Date |
Fri, 22 Jun 2012 09:48:21 +0100 |

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: >> * 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/ * * 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/

**Follow-Ups**:**RE: st: Re: st: Re: st: BOOTSTRAP: the standard errors of marginal effects of MIXLOGIT***From:*nagi kayo <kayonagi@hotmail.co.jp>

**References**:**st: BOOTSTRAP: the standard errors of marginal effects of MIXLOGIT***From:*nagi kayo <kayonagi@hotmail.co.jp>

**st: Re: st: BOOTSTRAP: the standard errors of marginal effects of MIXLOGIT***From:*Arne Risa Hole <arnehole@gmail.com>

**RE: st: Re: st: BOOTSTRAP: the standard errors of marginal effects of MIXLOGIT***From:*nagi kayo <kayonagi@hotmail.co.jp>

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