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From |
Muhammad Anees <anees@aneconomist.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Interpreting Coefficient of a count independent variable |

Date |
Mon, 16 Apr 2012 21:42:18 +0500 |

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 >> >> * >> * 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/ > > > * > * 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 * * 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/

**References**:**st: Interpreting Coefficient of a count independent variable***From:*Muhammad Anees <anees@aneconomist.com>

**Re: st: Interpreting Coefficient of a count independent variable***From:*Maarten Buis <maartenlbuis@gmail.com>

**Re: st: Interpreting Coefficient of a count independent variable***From:*Muhammad Anees <anees@aneconomist.com>

**RE: st: Interpreting Coefficient of a count independent variable***From:*Cameron McIntosh <cnm100@hotmail.com>

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