Notice: On March 31, it was announced that Statalist is moving from an email list to a forum. The old list will shut down on April 23, and its replacement, statalist.org is already up and running.
[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]
st: Fwd: Questions
Jorge Eduardo Pérez Pérez <firstname.lastname@example.org>
st: Fwd: Questions
Sun, 4 Sep 2011 11:52:32 -0400
I think this email was intended to go to Statalist.
Why not add a dummy variable for individuals in rural areas?
Jorge Eduardo Pérez Pérez
---------- Forwarded message ----------
From: Husaina Banu Kenayathulla <email@example.com>
Date: Sun, Sep 4, 2011 at 9:08 AM
To: Perez Perez Jorge Eduardo <firstname.lastname@example.org>
I am Husaina. I have some issues with my third analysis for my dissertation:
I run the OLS regression separately for male and female labor force.
My dependent variable is natural log of earnings (which includes
earnings from self-employed and wage work). My independent variable
is experience, exprience squared, years of scho0ling, ethnicity,
urban/rural, and region.
When I run the model and test for hetereskedasticity,my model violates
the constant variance assumption for residuals. Thus, I use robust std
erros. But, when I check for normality of residuals (using skewness
and kurtosis test in STATA), it also violates the normality
assumption. The q-q plot and p-plot is attached. If my normality
assumption is violated, my estimates is unbiased but all my testing is
invalid. I think the problem arise because I have large negative
outliers but these cases are valid (individuals in rural areas whose
income is very low). So, I can't drop them. They are also influential.
I am not sure what to do. Do you have suggestions? Can I write the
results when the normality assumption is violated?
Any advice will be great!
* For searches and help try: