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Re: st: probit questions
Maarten buis <email@example.com>
Re: st: probit questions
Wed, 25 Jun 2008 17:25:52 +0100 (BST)
--- "Sun, Yan (IFPRI)" <Y.SUN@CGIAR.ORG> wrote:
> I have couple of questions about the Probit model. My dependent
> variable is a 0/1 binary choice (1=invest in technology, 0=no
> investment) for user groups, independent variables are user groups'
> characteristics (around 20).
> 1) Which model is correct one: Probit or Logit? What is the STATA
> command for checking this?
The differences are very minor, I believe that the tails are slightly
fatter for the logistic distribution than the normal (or was it the
other way round?), anyhow you won't find any difference with only 60
observations. So your choice should be quided by convenience and
conventions in your discipline. I like -logit- because I like odds
ratios, but economists tend to like -probit-.
> 2) I have small observations (total 170 observations, but valid obs.
> Is only around 60 for all independent variables), sometimes the
> regression does not report report "wald chi2" statistics. What is
> the reason for this?
In these kinds of models trying to estimate the effects of 20
covariates with 60 datapoints just means that you are pushing your data
Related to this, loosing almost 2/3 of your already small dataset seems
very problematic to me. Do you realy need all 20 covariates? Also you
might consider Multiple Imputation, which in Stata is among other
implemented as the -ice- package by Patrick Royston (2004 2005a 2005b
2007). Although, given this large proportion of missing data, this too
has it's own problems.
> 3) I got a note after right after the regression, which says "8
> failures and 7 successes completely determined", what does this
It means that there are observations with the same values on all
covariates who are all failures or all successes. Standard maximum
likelihood can't handle these cases, -exlogistic- can, see
Hope this helps,
Royston, P. 2004. Multiple imputation of missing values. Stata Journal
Royston, P. 2005a. Multiple imputation of missing values: update. Stata
Journal 5(2): 188–201.
Royston, P. 2005b. Multiple imputation of missing values: Update of
ice. Stata Journal 5(4): 527-536.
Royston, P. 2007. Multiple imputation of missing values: further update
of ice, with an emphasis on interval censoring. The Stata Journal 7(4):
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
1081 HV Amsterdam
Buitenveldertselaan 3 (Metropolitan), room Z434
+31 20 5986715
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