If you're saying that your explanatory variable is education and you'd
like to know what happens to your dependent variable (earnings?) if
education is increased by 2 years, then you simply use setx to increase
education from its observed mean to the mean plus 2 years. Your chosen
quantity of interest will then change and you can observe what your model
is saying about that change.
if instead you have a two-stage model, with education the dependent
variable in one stage and then somehow the predicted values from that are
put into the next stage, then you could use clarify in the two stages
separately, with the first stage just producing the predicted values and
then clarify in the 2nd stage adding to that predicted education value.
Gary
---
Gary King
Institute for Quantitative Social Science
Harvard University, 34 Kirkland St, Cambridge, MA 02138
, email: King(a)Harvard.Edu
Direct 617-495-2027, Assistant 495-9271, eFax 812-8581
On Mon, 25 Apr 2005, Nicolas EPELE wrote:
Gary: Sorry for bother you again. Let me give you an
example: Ferreira
and Leite in Educational expansion and income distribution. A
Micro-Simulation for Cear� analyse what would happend if the mean
education would be 2 years greater (an expanding educational policy)
than the real mean. To generate this, they estimate an ordered probit to
know the relationship among years of schooling and some people
characteristics. They increase the education level respecting this
relationship (the distribution of education from this characteristics
resulting from the ordered probit) and they obtain a new vector of
education (with the same characteristics). So they can apply this new
education vector to an equation of earnings and estimate a
counterfactual poverty and inequality measures. This is what I'm looking
for, and that is my question to you if Clarify (that is a very
interesting package) can do this. Sorry for having at one's disposal
your time. Thank you very much again. Sincerely yours, Nicolas
Gary King <king(a)harvard.edu> wrote:
If I understand what you're saying, you have an ordinal probit
specification and you want to change your explanatory variable M such that
the predicted value (or expected category) of your dependent variable e
increases by 2. I think with Clarify, you'd need to do this 'by hand',
increasing M gradually until the prediction came out as you wanted it.
Doing this via a bisection search would be the quickest strategy I'd
guess.
Gary
---
Gary King
Institute for Quantitative Social Science
Harvard University, 34 Kirkland St, Cambridge, MA 02138
http://GKing.Harvard.Edu, email: King(a)Harvard.Edu
Direct 617-495-2027, Assistant 495-9271, eFax 812-8581
On Mon, 25 Apr 2005, Gary King wrote:
---------- Forwarded message ----------
Date: Mon, 25 Apr 2005 08:28:59 -0500 (CDT)
From: Nicolas EPELE
To: King(a)Harvard.Edu
Subject: Professor Gary King, request
Professor Gary King:
My name is Nicolas Epele. I'm writing from
Argentina, I'm economist from Universidad Nacional de La Plata and I'm trying
to use the Clarify package in Stata. I'm trying to do the next simulation: I
have a dataset of micro-data with years of schooling, e, an characteristics,
M. I'm running the ordered probit model OP(e | M). So would like to generate
an e' vector of years of schooling with a mean 2 years greater than the mean
of vector e, but preserving the relation established in the model. So if I
run OP(e' | M), the coefficient will be similar to OP(e | M) model. I'm
writing to know if Clarify can do that.
Thanks in advance and thank you very much for
sharing the Clarify package. Sorry for my english.
Faithfully yours, Nicolas
---------------------------------
Do You Yahoo!?
Todo lo que quieres saber de Estados Unidos, Am�rica Latina y el resto del
Mundo.
Vis�ta Yahoo! Noticias.
---------------------------------
Do You Yahoo!?
Todo lo que quieres saber de Estados Unidos, Am�rica Latina y el resto del Mundo.
Vis�ta Yahoo! Noticias.