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

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

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 		 	   		  

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