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From |
nagi kayo <[email protected]> |

To |
statalist質問用 <[email protected]> |

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

Date |
Fri, 22 Jun 2012 16:34:38 +0900 |

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: [email protected] > To: [email protected] > > 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 <[email protected]> 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/

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**References**:**st: BOOTSTRAP: the standard errors of marginal effects of MIXLOGIT***From:*nagi kayo <[email protected]>

**st: Re: st: BOOTSTRAP: the standard errors of marginal effects of MIXLOGIT***From:*Arne Risa Hole <[email protected]>

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