Re: st: Interpreting Coefficient of a count independent variable

[Muhammad Anees <anees@aneconomist.com>]

Dear Cameron,

These are really valuable for me. I would thank you specially for
always giving me the more information.

Thanking again,
Anees

On Mon, Apr 16, 2012 at 7:15 PM, Cameron McIntosh <cnm100@hotmail.com> wrote:
> A few additional observations from the peanut gallery... I might also suggest thinking about instrumenting the smoking predictor, as it's certainly endogenous:
>
> Lochner, L., & Moretti, E. (May 2011). Estimating and Testing Non-Linear Models Using Instrumental Variables. NBER Working Paper No. 17039.  http://emlab.berkeley.edu/~moretti/nonlinearities.pdf
>
> Hausman, J.A. (1983). Specification and estimation of simultaneous equation models. In Z. Griliches & Intriligator, M.D. (Ed.), Handbook  of  Econometrics (vol I, pp. 391-448). North-Holland Publishing Company.http://pria.uran.ru/ebooks/Unsorted/07%20Hausman%20-%20Specification%20and%20Estimation%20of%20Simultaneous%20Equation%20Models.pdf
>
> Abrevaya, J., Hausman, J.A., & Khan, S. (2010). Testing for Causal Effects in a Generalized Regression Model With Endogenous Regressors. Econometrica, 78(6), 2043–2061.
>
> Baum, C.F., Schaffer, M.E., & Stillman, S. (2007). Enhanced routines for instrumental variables/generalized method of moments estimation and testing. The Stata Journal, 7(4), 465-506.http://www.stata-journal.com/sjpdf.html?articlenum=st0030_3
>
> Bascle, G. (2008). Controlling for endogeneity with instrumental variables in strategic management research. Strategic Organization, 6(3), 285-327.
>
> Gennetian, L. A., Magnuson, K., & Morris, P. A. (2008). From statistical associations to causation: What developmentalists can learn from instrumental variables techniques coupled with experimental data. Developmental Psychology, 44(2), 381-394.
>
> Larcker, D. F., & Rusticus, T. O. (2010). On the use of instrumental variables in accounting research. Journal of Accounting and Economics, 49(3), 186-205.
>
> Sovey, A.J., & Green, D.P. (2011). Instrumental Variables Estimation in Political Science: A Readers' Guide. American Journal of Political Science, 55(1), 188-200.
>
> Also consider that the smoking-income relations may be reciprocal, as you seem to note (in which case I think you would need to instrument both smoking and income):
>
> Paxton, P., Hipp, J.R., & Marquart-Pyatt, S. (2011). Nonrecursive Models: Endogeneity, Reciprocal Relationships, and Feedback Loops. Quantitative Applications in the Social Sciences, Volume 168. Thousand Oaks, CA: Sage.
>
> Wright, S. (1960). The treatment of reciprocal interactions, with or without lag. Biometrics, 16, 423-445.  http://www.ssc.wisc.edu/soc/class/soc952/Wright/Wright_The%20Treatment%20of%20Reciprocal%20Interaction,%20with%20or%20without%20Lag,%20in%20Path%20Analysis.pdf      Wong, C-S., & Law,
> K.S. (1999). Testing reciprocal relations by nonrecursive structural equation models using cross-sectional data. Organizational Research Methods, 2(1), 69-87.
>
> Cam
>
>> Date: Mon, 16 Apr 2012 15:03:43 +0500> Subject: Re: st: Interpreting Coefficient of a count independent variable
>> From: anees@aneconomist.com
>> To: statalist@hsphsun2.harvard.edu
>>
>> Thanks Maarten,
>>
>> You have given me the right direction. It works. Thanks again
>>
>> Anees
>>
>> On Mon, Apr 16, 2012 at 1:18 PM, Maarten Buis <maartenlbuis@gmail.com> wrote:
>> > On Mon, Apr 16, 2012 at 7:24 AM, Muhammad Anees wrote:
>> >> I am trying to figure out how smoking affects earnings contradictory
>> >> to the normal routine of studying the affects of earnings on smoking
>> >> in my previous studies.
>> >> I have run the following result and I want to know the possible
>> >> interpretational issues with a count independent variables.
>> >
>> > None, other than the usual caveat that the effects may not be linear.
>> > I typically start with  a -glm- with the log link for a variable like
>> > income. I would than try adding an extra indicator variable for
>> > non-smokers as these are in all likelihood very different the rest of
>> > the curve. After that I would try various linear splines to capture
>> > any additional non-linearity. Consider the example below:
>> >
>> > *----------------- begin example -------------------
>> > sysuse nlsw88, clear
>> >
>> > gen c_hours = hours - 40
>> > mkspline t40 0 mt40 = c_hours
>> > gen byte normal = hours == 40 if hours < .
>> >
>> > gen marst = !never_married + 2*married
>> > label var marst "marital status"
>> > label define marst 0 "never married"    ///
>> >                   1 "widowed/divorced" ///
>> >                   2 "married"
>> > label value marst marst
>> >
>> > gen c_grade = grade - 12
>> >
>> > glm wage t40 mt40 normal c_grade i.race i.marst ///
>> >    c.ttl_exp##c.ttl_exp c.tenure##c.tenure     ///
>> >    , link(log) eform
>> >
>> > preserve
>> > replace c_grade = 0
>> > replace race = 1
>> > replace marst = 0
>> > replace ttl_exp = 0
>> > replace tenure = 0
>> >
>> > bys hours : keep if _n == 1
>> >
>> > predict yhat, mu
>> >
>> > twoway line yhat hours
>> > restore
>> > *------------------ end example --------------------
>> >
>> > I would interpret these results as follows: If someone works less than
>> > 40 hours a week her hourly wage will increase by (1-1.02)*100%=2% for
>> > every hour extra worked. There is a sudden (but non-significant) drop
>> > in this curve at 40 hours of (1-.98)*100%= -2%. After 40 hours a week
>> > the hourly wage will drop a non-significant 1% per hour extra worked.
>> >
>> > This story is also shown in the graph. I guess that it should also be
>> > possible to also do this with -margins- and -marginsplot-, but I was
>> > unsuccessful, so I fell back to old and trusted -predict- solution.
>> >
>> > Hope this helps,
>> > Maarten
>> >
>> > --------------------------
>> > Maarten L. Buis
>> > Institut fuer Soziologie
>> > Universitaet Tuebingen
>> > Wilhelmstrasse 36
>> > 72074 Tuebingen
>> > Germany
>> >
>> >
>> > http://www.maartenbuis.nl
>> > --------------------------
>> > *
>> > *   For searches and help try:
>> > *   http://www.stata.com/help.cgi?search
>> > *   http://www.stata.com/support/statalist/faq
>> > *   http://www.ats.ucla.edu/stat/stata/
>>
>>
>>
>> --
>>
>> Best
>> ---------------------------
>> Muhammad Anees
>> Assistant Professor/Programme Coordinator
>> COMSATS Institute of Information Technology
>> Attock 43600, Pakistan
>> http://www.aneconomist.com
>>
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>
>
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-- 

Best
---------------------------
Muhammad Anees
Assistant Professor/Programme Coordinator
COMSATS Institute of Information Technology
Attock 43600, Pakistan
http://www.aneconomist.com

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