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From |
Cameron McIntosh <cnm100@hotmail.com> |

To |
STATA LIST <statalist@hsphsun2.harvard.edu> |

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

Date |
Mon, 16 Apr 2012 10:15:18 -0400 |

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/

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

**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>

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