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Re: st: Ordered Probit Regression with censored values


From   Phuong Lan Nguyen <phuong@u.washington.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Ordered Probit Regression with censored values
Date   Thu, 20 Apr 2006 14:11:41 -0700 (PDT)

Dear Maarten,

thank you very much for your email. I did use this method for my study on social stratification. It is very helpful way to deal with right censoring values. However, I plan to use sample of kids aged 15-18 years old. that means, they have not completed their high school. I found out that I may be able to use ordered probit regression if the dependent variable can be controlled for censored values. Do you have any hint on this?I only found help in creating censored values for tobit model online.

Thanks.
phuong



On Thu, 20 Apr 2006, Maarten buis wrote:


Phuong:
The easiest way to deal with this problem is to estimate what is known in the social
stratification literature as a Mare model (Mare 1980 and Mare 1981). Say the educational system
you study has four levels, and everybody has to finish all lower levels in order to obtain a
certain level, than knowing someones highest eachieved level of education also implies knowing all
transitions that persons must have passed in order to get there. So a person who has finished
level two, must have passed the first and second transition. The Mare model models the probability
of passing a transition. You can estimate one by making a three dummie variables: one that equals
one if the person passed the first transition and zero if he/she fails, one that equals one if the
preson passed the second transition, zero if he/she fails, and missing if he/she failed the first
transition, and one that equals one if a person passed the third transition, zero if he/she fails,
and missing if he/she failed either the first or second transition. Estimate a separate -logit- or
-probit- on each variable. See the example below.

Big advantage for you is that it deals with right censoring in a quite natural way, censored cases
can be dealt as any other as long as you know the highest achieved level of education at time of
the interview. Disadvantage is that now you don't get one effect for each explanatory variable but
as many effects as there are transitions.

HTH,
Maarten

Mare, Robert D. 1980. "Social Background and School Continuation Decisions." Journal of
the American Statistical Association 75(370), pp. 295-305.
Mare, Robert D. 1981. "Change and Stability in Educational Stratification." American Sociological
Review 46(1), pp. 72-87.


*------------begin example--------------
sysuse nlsw88, clear

/*preliminary data prep*/
tab grade
gen ed = grade>=12
replace ed = 2 if grade >=13 & grade <16
replace ed = 3 if grade >=16
tab ed
tab race
gen white = race == 1

/*generate transition dummies*/
gen ed01 = ed>=1
gen ed12 = ed>=2 if ed>=1
gen ed23 = ed>=3 if ed>=2

/*estimate the Mare model*/
logit ed01 white south
logit ed12 white south
logit ed23 white south
*--------------end example--------------

--- Phuong Lan Nguyen <phuong@u.washington.edu> wrote:
I am working on years of schooling variable for all individuals who are in
school or already completed their education. Since I plan to run ordered
probit regression, I guess I need to have a special command for the
censored values in the ordered probit regression. Does anyone run it
before? Please give me advice on how to set up the dependent variable and
the ordered probit regression.


-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands

visiting adress:
Buitenveldertselaan 3 (Metropolitan), room Z214

+31 20 5986715

http://home.fsw.vu.nl/m.buis/
-----------------------------------------



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---)*(---)*(---)*(---)*(---)*(---)*(---)*(---)*(---
Phuong Lan Nguyen
Department of Sociology
202 Savery, UW
Seattle WA 98195-3340
Email: phuong@u.washington.edu



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