## dependent var with zeros and fixed effects or clustering (symmetrically trimmed least squares??)

### dependent var with zeros and fixed effects or clustering (symmetrically trimmed least squares??)

Hello everyone,

I am writing because I was wondering what procedure I could use in SAS
or other statistical software to analyze a dependent variable that has
many zeros, if at the same time I want to use fixed effects or
clustering in the regression equation (because it is a very short
[i.e. 2-period] panel).  The fixed effects or clustering does not
allow one to use Tobit.  I saw a reference to symmetrically trimmed
least squares for this situation.  Any ideas how I could do this or an
alternative estimation for this problem?

matt

### dependent var with zeros and fixed effects or clustering (symmetrically trimmed least squares??)

Quote:> I am writing because I was wondering what procedure I could use in SAS
> or other statistical software to analyze a dependent variable that has
> many zeros, if at the same time I want to use fixed effects or
> clustering in the regression equation (because it is a very short
> [i.e. 2-period] panel).  The fixed effects or clustering does not
> allow one to use Tobit.  I saw a reference to symmetrically trimmed
> least squares for this situation.  Any ideas how I could do this or an
> alternative estimation for this problem?

Have a look at limdep. See, http://www.limdep.com/programfeatures_crosssection.shtml
Some of these procedures (but not all) can be done in SAS.

Dear newsgroup members,

We have a problem comparing the strength of regression coefficients of the
same independent variable in models with different dependent variables.
Below I will show two (related) examples:

Example 1.
Cross-sectional design. Dependent variables: two speed tasks (number of
seconds needed is recorded; the lower, the better, the faster) and tasks
differ in complexity/difficulty. Independent variable: age (0: young and 1:
old). Question: do older people particularly differ from younger people in

Example 2.
Cross-sectional design. Dependent variables: one speed task (number of
seconds needed is recorded; the lower, the better, the faster) and a memory
task (number of words reproduced after presentation; the higher, the better
(the memory)). Independent variable: age (0: young and 1: old). Question: do
older people particularly differ from younger people in the speed task (more
so than in the easy task)?

How can these effects of age be compared? In the first example both
dependent variables are in seconds, in the second the scales differ
completely. It seems to me that standardisation of at least the dependent
variable is needed? But how to test for a significant difference in the
effect of age.

Some have suggested MANOVA or GLM repeated measures designs. Is this the way
to go? And, foremost, should at least the dependent variable be standardised
then?

Thank you for any information.

Best regards,

Hans Bosma