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Re: st: Bootstrap question


From   May Boggess <mboggess@stata.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Bootstrap question
Date   15 Feb 2005 08:40:00 -0600

On Tuesday, Raoul asked about bootstrap:

> I run a probit regression model such as
> probit job age educat
> I use predictnl to predict special cases, e.g. for age==32 and educat=3.  
> Predictnl xb= _b[age]*32+_b[educat]*3
> Now, I want to get bootstrapped CI's for the point estimate of xb. 
> I read the manual but I do not understand how to handle the scalar xb?
> 

First of all, it would a good idea for Raoul to check the formula he
used above for xb. Here is an example:

 clear
 set obs 100
 set seed 12345
 gen job=uniform()>0.5
 gen age=uniform()*50+5 
 gen educat=uniform()*12+1
 probit job age educat 
 replace age=32
 replace educat=3
 predict xb,xb
 gen myxb=_b[age]*32+_b[educat]*3+_b[_cons]
 sum xb myxb

So we see that Raoul forgot the constant term in xb. Before moving on to
-bootstrap-, we can use -predictnl- to obtain the standard error via the
delta method. To do so we can rely on Stata to predict xb for us, so
there is no chance of an arithmetic mistake:

 clear
 set obs 100
 set seed 12345
 gen job=uniform()>0.5
 gen age=uniform()*50+5
 gen educat=uniform()*12+1
 probit job age educat
 replace age=32
 replace educat=3
 predictnl xb=predict(xb), se(se)
 list xb se in 1

To get the boostrapped standard error we need to write a program that
returns xb in the r() results:

 capture program drop myboot
 program myboot, rclass
 probit job age educat
 return scalar xb=_b[age]*32+_b[educat]*3+_b[_cons]
 end

 clear
 set obs 100
 set seed 12345
 gen job=uniform()>0.5
 gen age=uniform()*50+5
 gen educat=uniform()*12+1
 bootstrap "myboot" xb=r(xb), reps(100)

 -- May
 mboggess@stata.com

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