Re: st: control a variable in stata

[Nick Cox <njcoxstata@gmail.com>]

The research question is explaining salaries. That being so you would  
be better off with -poisson- or -glm, link(log).

OLS is an estimation method, not a model.

Nick

On 21 Apr 2012, at 13:33, "Kong, Chun" <C.Y.Kong@warwick.ac.uk> wrote:

> Dear Nora,
>
> Thank you very much for your advice!! I really appreciate for your  
> time and help :)
> I am going to add a race and age variable and see how they affect on  
> player's salary. I have look through the paper you have suggested  
> and other studies with the related topic and they gave me many great  
> ideas!!
> Regarding the choice of model, do you mean that OLS is the  
> appropriate and the only model I should if I only have data in 1  
> season??
>
> Thank you very much for your help again!
>
> Yours sincerely
> Andy
> ________________________________________
> From: owner-statalist@hsphsun2.harvard.edu [owner- 
> statalist@hsphsun2.harvard.edu] on behalf of Nora Reich  
> [nhmreich@googlemail.com]
> Sent: 20 April 2012 17:15
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: control a variable in stata
>
> Dear Andy,
>
> first some ideas about your independent variables:
>
> 1. You distinguish between players born in the US and players born
> outside the US. I would suggest to also control for skin colour (black
> or white), either only for those born in the US or for all (depending
> on the results of these estimations), because skin colour seems to
> affect the salary as well, see, for example, this paper:
> http://business.uni.edu/economics/Themes/rehnstrom.pdf (which I found
> by simply googling).
>
> 2. Another important factor might be the number of years the player
> has played in the NBA.
>
> 3. Maybe age also plays a role?
>
> Generally, my advice would be to look at papers with a similar
> research question and derive your list of independent variables from
> the literature review (and, of course, from own ideas). Have you done
> a literature review? This is usually a good thing to do before
> estimating regressions.
>
> Random effects and fixed effects models are for panel data. If you
> have only 1 NBA season, these models are not appropriate. However, if
> you have a variable "year" which tells you whether the data is from
> 2010 or 2011, it would be valuable to include a dummy for one of the
> years in your regression.
>
> For the tests for the assumptions of the OLS model, just google
> something like "regress postestimation stata".
>
> Best regards
> Nora
>
>
>
> Am 20. April 2012 16:11 schrieb Kong, Chun <C.Y.Kong@warwick.ac.uk>:
> > Dear statalist,
> >
> > I am working on a paper in finding the determinants of NBA players'  
> > salary. Data are collected from the 2010-2011 NBA season. My  
> > dependent variable is ln(salary). I have got several dummy variables
> >
> > 1) ethnicity (0 if player is born in US, 1 for international player)
> > 2)All-Star
> > 3)Season Played in the NBA
> > 3)Efficiency Index
> > 5)Approximate Value Index
> > 6)Versatility Index
> > 7)Points per Field Goal
> > 8)Turnover to assist Ratio
> >
> > My results turn out that the salary of international player is  
> > higher relative to the players who born in US. However, to make the  
> > comparison fair, I want to test the effect of ethnicity on player's  
> > salary while controlling the performance of both international  
> > players and US players.
> >
> > At the moment, I am now only working on a simple OLS model. May I  
> > ask for your advice that what can I try or do to make my results  
> > better? ( I have read something like the random effect and fixed  
> > effect model, but I am really not sure what I can do). Also, do I  
> > need to do some tests to check the problem such as endogeneity in  
> > my model
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