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Re: st: control a variable in stata


From   Nick Cox <njcoxstata@gmail.com>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   Re: st: control a variable in stata
Date   Sat, 21 Apr 2012 17:05:21 +0100

For more on why, see

http://blog.stata.com/2011/08/22/use-poisson-rather-than-regress-tell-a-friend/

and its discussion.

On Sat, Apr 21, 2012 at 1:54 PM, Nick Cox <njcoxstata@gmail.com> wrote:
> 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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