It seems to me that income seems to be causally affected by oil whether they
are oil-dependent or not. For example, countries would be richer if they had
oil. The problem is that we don't know how rich Afganistan is if it had oil.
This is the fundametal problem of causal inference: You only observe one
outcome (if they didn't have oil) and not the other (if they did have oil).
Kosuke
Quoting Stanislav Markus <smarkus(a)fas.harvard.edu>:
> My general question is about controlling for a variable that is causally
> prior to the key quantity of interest in *some* observations, but not in
> others - when the two categories of observations are known ex ante.
>
> Specifically for our article, we are examining the effect of oil on
> democracy - and deciding whether to control for income. Income is
> probably causally posterior to oil in highly oil-dependent countries
> (and hence shouldn't be controlled for), but causally independent/prior
> to oil in others (and hence should be added as a control) - can we
> somehow "weigh" the control variable to account for this? Or, if the
> choice is between keeping or dropping it, and we choose the latter,
> should we argue that the degree of any induced omitted variable bias is
> less severe than the bias from post-treatment control variable would
> have been had we kept it..?
>
> Grateful,
> Stan
>
> p.s. Amelia is still kaput, crashing after 100 iterations with same
> error message..
>
>
> ****************************
>
> Stanislav Markus
> Ph.D. Candidate
>
> Harvard University
> Department of Government
>
> e: smarkus(a)fas.harvard.edu
> t: 617.513.5407
>
>
>
> -----Original Message-----
> From: gov2001-l-admin(a)fas.harvard.edu
> [mailto:gov2001-l-admin@fas.harvard.edu] On Behalf Of Kosuke Imai
> Sent: Tuesday, April 29, 2003 1:11 PM
> To: gov2001-l(a)fas.harvard.edu
> Subject: [gov2001-l] office hrs (fwd)
>
>
> I will have a regular office hour today from 4 to 6pm.
>
> Kosuke
>
> ---------- Forwarded message ----------
> Date: Tue, 29 Apr 2003 11:58:30 -0400 (EDT)
> From: Yevgeniy Kirpichevsky <kirpich(a)fas.harvard.edu>
> To: Kosuke Imai <kimai(a)fas.harvard.edu>
> Subject: office hrs
>
> Kosuke,
> will you be in the office today @4?
>
> -y and p
>
> _______________________________________________
> gov2001-l mailing list
> gov2001-l(a)fas.harvard.edu
> http://www.fas.harvard.edu/mailman/listinfo/gov2001-l
>
> _______________________________________________
> gov2001-l mailing list
> gov2001-l(a)fas.harvard.edu
> http://www.fas.harvard.edu/mailman/listinfo/gov2001-l
>
----- End forwarded message -----
Does anyone know a graphics program (accessible on campus or
otherwise free) that will allow me to draw a simple plot of an
axis with a couple of lines on it? That will save as a .eps
file for inclusion in my latex document? I tried Paintshop and
the quality is really poor...like my text is all fuzzy. Any
other suggestions?
Thanks, Olivia.
sure, that's a good approach.
Gary
On Sat, 10 May 2003, Maria Popova wrote:
> Hi Gary and Kosuke,
>
> We want to check the causal mechanism and we're using a mnl. Do we just
> include all the variables that we think are causally posterior to our main
> variable of interest and look for the coefficient of the main variable of
> interest to drop as close to 0 as possible?
>
> Thanks!
> Maria and Stan
>
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For any type of model you estimate, is it true that ideally, plotting =
the residuals shows n points scattered about the y=3D0 line with no =
apparent pattern or is this only true for linear regression?
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<DIV><FONT face=3DArial size=3D2>For any type of model you estimate, is =
it true that=20
ideally, plotting the residuals shows n points scattered about the y=3D0 =
line with=20
no apparent pattern or is this only true for linear=20
regression?</FONT></DIV></BODY></HTML>
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I am trying to estimate the simulated betas for a logit regression with 5
imputed datasets. Instead of getting 5000 simulated values (1000 for each
dataset, which is the default), I only get 1000 in total. Here are the
steps I followed:
use "C:\imp1.dta", clear
estsimp logit winner racebl racehi raceas inchi incmed, robust
mi(C:\imp1.dta, C:\imp2.dta, C:\imp3.dta, C:\imp4.dta, C:\imp5.dta)
At this stage, when I do "sum", it lists the newly created variables along
with the variables in imp1.dta, and there are only a 1000 observations.
Ultimately, I would like to "rbind" all the coefficients from the 5
datasets, and export them to R. How do I do that?
Thanks,
Nirmala
**********************************************************************
Nirmala Ravishankar
First Year Graduate Student,
Government Department,
Harvard University
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I need to simulate some predicted values and must estimate a variance =
covariance matrix from five imputed data sets. Does anybody know the =
formula for estimating the five matrices?
Traci
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<DIV><FONT face=3DArial size=3D2>I need to simulate some predicted =
values and must=20
estimate a variance covariance matrix from five imputed data=20
sets. Does anybody know the formula for estimating the =
five=20
matrices?</FONT></DIV>
<DIV><FONT face=3DArial size=3D2></FONT> </DIV>
<DIV><FONT face=3DArial size=3D2>Traci</FONT></DIV></BODY></HTML>
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Hey, all:
Here's some code we wrote to try to calculate the robust standard errors
for a logit regression of the binary "winner" on the dataset X, consisting
of 18 independent variables and a column of ones. It produces a sensible
19x19 variance-covariance matrix, but the square root of the diagonal
elements of V are much larger than the authors'. Any thoughts on this
code would be much appreciated.
Thanks,
Ryan & Nirmala
reg <- glm(winner ~ racebl + racehi + raceas + inchi + incmed + edhs +
edcol + edba + agec1 + agec4 + sex + margin + regla + regbay + regsc +
libcon+ pdem + poth, data = zol, family = binomial(logit))
X <- zol[,c(3,4,5,24,23,11,12,13,6,9,25,26,14,16,15,1,19,21)]
X <- as.matrix(cbind(c(1),na.omit(X)))
a <- matrix(NA,nrow(X),1)
for(i in 1:nrow(X)){
u.sq <- (reg$residuals[i])^2
x.pr.x <- t(X[i,])%*%X[i,]
a[i] <- u.sq*(x.pr.x)
}
V <- solve(t(X)%*%X)%*%(sum(a)*solve(t(X)%*%X))
--
------------------------------------------
Ryan T. Moore ~ Government & Social Policy
Ph.D. Candidate ~ Harvard University
All,
I trust your papers are coming along nicely. Just one question.
We are dealing with an article about the impact of international organizations
on democratization. The author argues that as a state joins international
organizations, it is more likely to democratize itself. His explanatory
variable is the international organization score, which is the score of the
most *democratic* international organization, of which a state is a member.
Now my question is by taking the score of the most democratic international
organization, isn't the author selecting on DV? Of course, the country that
joins a democratic international organization is more likely to democratize
itself than one that does not join. And the more democratic the organization
is, the more likely the country is to democratize.
Any comments? My thought is that he is selecting on DV.
yongwook
-----------------------------
Yongwook Ryu
PhD Student
Department of Government
Harvard University
Tel:617-493-3397
Email: yryu(a)fas.harvard.edu
-----------------------------