Clarify

clarify@lists.gking.harvard.edu

166 discussions

by mm973＠columbia.edu

Dear all I am estimating the effect of living in an HIV household on the probability of attending secondary school. I am using a simple probit equation as well as estsimp probit and Stata 9.2. After reading King, Tom and Wittenberg (2000), I am relying on graphing predicted probabilities and taking into account confidence intervals in presenting/interpreting results. I am trying to generate a graph similar to Figure 1 (Probability of voting by age) in the aforementioned paper. After simple probit I generate a graph using prgen and it works. The problem is that after estsimp probit, when I use the following command: graph plo phi AgeYaxis, s(ii) c(||) I get the following message indicating that I am using Stata7 syntax: plograph_g.new phi ageaxis, s(ii) c(||): class member function not found If I do the following, I get same graph as prgen but i still do not get the confidence intervals to plot gr7 plo phi AgeYaxis, s(oo) c(ll) ylabel(0(.1)1) Any suggestions. Attached is my syntax and graphs best Moumie

15 years, 7 months

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15 years, 7 months

Confidence intervals for first differences

by Bennet A. Zelner

Hello, I posted a somewhat lengthy query to this list last week. Since no one has posted a response, I thought that I would post a clearer (and slightly more succinct) version of my query. (I also realize that the lack of a response may reflect the fact that people are on vacation right now.) It is well known that in the logit model (as in other nonlinear models), the value of the predicted probability Y1 associated with a given value of the independent variable X1 depends not only on X1's value, but also on the values of the other independent variables in the model. The standard error of Y1 also varies depending on both X1's value as well as the values of the other independent variables in the model. Analogous results obtain for dY1, the first difference associated with a given discrete change in the value of X1: both dY1 and SE(dY1) depend on the starting value of X1 as well as the values of the other independent variables in the model. These results can be confirmed using CLARIFY. For a given discrete change dX1, the value of dY1, the standard deviation of dY1, and the values of the lower and upper bounds of the p% confidence interval for dY1 depend on the values of the other independent variables in the model. What I find to be counterintuitive is that, if the starting value of X1 is held constant, then even though the values of the bounds of the p% confidence interval for dY1 change depending on the values of the other independent variables, the *relative* width of this confidence interval does not change. For example, if dY1 is significant at the 91.0% level for a given value of dX1, a given starting value for X1, and given values of the other variables, dY1 will always be significant at the 91.0% level for this value of dX1 and this starting value fox X1, *regardless of the values of the other variables in the model.* In conventional terms, this would mean that the values SE(dY1) and dY1 change in proportion to one another when the values of the other variables are allowed to vary, so that the z-statistic dY1/SE(dy1) remains constant. As I stated in my earlier email, my intuition that this should not be the case may simply be incorrect. However, this result does not hold when I calculate differences in predicted values and their standard errors using conventional techniques (even though I realize that conventional techniques are subject to approximation error and bias). One possibility is that the confidence intervals I obtain for dY1 using CLARIFY do not fully reflect the estimation uncertainty surrounding the estimated coefficients on the other independent variables in the model (i.e., X2,...,XN). If anyone can provide me with insight into this issue, I would be most appreciative. Bennet Zelner -- Bennet A. Zelner Duke University Fuqua School of Business Box 90120 Durham, NC 27708-0120 bzelner(a)duke.edu Tel +1 919 660-1093 Fax +1 919 681-6244

15 years, 9 months

Confidence interval variability

by Bennet A. Zelner

Hello, I am hoping that someone could shed some light on a puzzle that I have discovered when using Clarify to interpret results from a logit model. It is well known that the effect of a changing the value an independent variable x1 in a logit model depends on this variable's starting value, as well as the values of the other independent variables in the model. Clearly, the value of the lower and upper bounds of the p% confidence interval surround x1's estimated effect must vary as well. Intuitively, one would expect that x1's significance level might change too. My question regards this last point. Specifically, I have found whenever I use Clarify to assess the first difference (in the predicted probability) of a given change in the value of x1, the significance level of this effect never varies, regardless of what values the other independent variables in my model take. However, when I use conventional analytical techniques to calculate the same first difference and assess whether this estimated effect differs significantly from zero, I find that the significance level change of the estimated effect does change. I understand that the simulation-based approach is more precise because the delta method produces an approximation, and also because the simulation-based method implicitly corrects for bias in the logit formula for calculating predicted probabilities. However, because the results produced using the simulation-based method conflict with my intuition, I want to make sure that I am not missing something. One thought that occurs to me is that, depending on the mechanics of the -setx- command, the uncertainty surrounding the values of the other independent variables is somehow not being taken into account when assessing a change in the value of x1. This would not be an issue with the delta method because, even though this technique produces an approximation, it is one that reflects the multivariate nature of the equation. I have constructed a simple example using a publicly available Stata dataset to illustrate. sysuse auto, clear set seed 12345 gen x1 = round(uniform()) logit for x1 mpg length price mfx, at(mpg=17) mfx, at(mpg=30) The output shows that the p-value associated with a unit change in the dummy variable x1 is .576 when mpg = 17, and .604 when mpg = 30. estsimp logit for x1 mpg, sims(1000) forvalues clev = 61/63 { di `clev' setx mpg 17 simqi, fd(prval(1)) changex(x1 0 1) l(`clev') setx mpg 30 simqi, fd(prval(1)) changex(x1 0 1) l(`clev') } The outputs shows that, regardless of the value of mpg, the effect of a unit change in x1 differs significantly from zero for p = .61, but not when p >= .62. I realize that this example is trivial, but I have found more substantial differences using my own data. I'd very much appreciate it if someone could tell me whether the problem is with Clarify or with my intuition. Regards, Bennet Zelner -- Bennet A. Zelner Duke University Fuqua School of Business Box 90120 Durham, NC 27708-0120 bzelner(a)duke.edu Tel +1 919 660-1093 Fax +1 919 681-6244 -- Clarify mailing list served by Harvard-MIT Data Center [Un]Subscribe/View Archive: http://lists.gking.harvard.edu/?info=clarify

15 years, 9 months

Clarify problem in Stata

by Kenneth Wald

I was hoping somebody could detect the source of an annoying Stata error when I try to use estsimp with mlogit and multi-imputed datasets. Here's the command (edited to reduce the number of independent variables): estsimp mlogit gltialar age education female nonwhite, noconstant basecategory(2), mi(outdat1 outdat2 outdat3 outdat4 outdat5) This gives me the generic Stata error, "no variables defined" and the 11 return code. I have Stata set on the proper directory and it contains the files outdat1.dta through outdat5.dta. Before running estsimp, I can run the miest from the same directory and it does just fine. It seems to me that it's not reading the datasets but I can't figure out why from the Clarify documentation. Thanks. Ken Wald -- Kenneth D. Wald Distinguished Professor of Political Science University of Florida POB 117325 Gainesville, FL 32611-7325 Voice Mail: 352-273-2391 Home Page: web.clas.ufl.edu/users/kenwald -- Clarify mailing list served by Harvard-MIT Data Center [Un]Subscribe/View Archive: http://lists.gking.harvard.edu/?info=clarify

15 years, 10 months

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16 years

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16 years, 2 months

Clarify XT commands

by J.M.Chwieroth＠lse.ac.uk

Clarify seems to be incompatible with the XT commands for TSCS data in Stata10. Is there a way around this problem, or is something being developed? Jeffrey Chwieroth Lecturer in International Political Economy Department of International Relations London School of Economics Houghton Street London WC2A 2AE United Kingdom Tel: +44 (0) 20 7955 7209 Fax: +44 (0) 20 7955 7446 Website: http://personal.lse.ac.uk/chwierot/ Please access the attached hyperlink for an important electronic communications disclaimer: http://www.lse.ac.uk/collections/secretariat/legal/disclaimer.htm -- Clarify mailing list served by Harvard-MIT Data Center [Un]Subscribe/View Archive: http://lists.gking.harvard.edu/?info=clarify

16 years, 2 months

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by �ֶؼ��

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16 years, 3 months

Compute results from multiply imputed dataset- STREG

by Marguerite Duponchel

Hi, I am trying to compute results from multiply imputed datasets with Amelia for a survival analysis model with a log log distribution. It seems like clarify doesn't support streg, is there any other way with Stata? Thanks, -- Marguerite ---------------------------------------------------------------- This message was sent using IMP, the Internet Messaging Program. -- Ce message a ete verifie par MailScanner pour des virus ou des polluriels et rien de suspect n'a ete trouve. -- Clarify mailing list served by Harvard-MIT Data Center [Un]Subscribe/View Archive: http://lists.gking.harvard.edu/?info=clarify

16 years, 3 months

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