Dear Applied Statistics Community,
There will be no workshop on 11/5 (apparently Guy Fawkes' Day or the
anticipated effects of Election night scared away presenters).
But please join us on 11/12 when Kosuke Imai, Department of Politics,
Princeton University, will be presenting.
Cheers
Justin Grimmer
Dear Applied Statistics Community,
Please join us this Wednesday, October 29th, when Michael Kellerman, PhD
Candidate in the Department of Government, will present his work on "Electoral
Punishment as Signaling in Subnational Elections". Mike provided the
following abstract,
It is a well-established empirical regularity that parties in federal
office suffer setbacks in state-level elections. Many authors attribute this
to a desire on the part of voters to balance the policy preferences of the
federal incumbent. In this paper, I consider an alternative explanation with
a long tradition in the literature: voters punish the party of the federal
incumbent in state elections in order to send a signal to the federal
government. I construct a simple signaling model to formalize this
intuition, which predicts that under most circumstances signaling can occur
at only one level of government. I estimate a statistical model allowing for
electoral punishment using data from German elections and find support for
punishment at the state level, rather than the punishment at both levels
implied by balancing theories.
Mike also provided a copy of his paper, available here:
http://people.fas.harvard.edu/~kellerm/balsig.pdf
The applied statistics workshop meets each Wednesday in room K-354
CGIS-Knafel, 1737 Cambridge St, Cambridge MA. The workshop convenes at 12
noon with a light-lunch, presentations usually begin around 1215 and
conclude by 130 pm. As always, everyone is welcome!
Cheers
Justin Grimmer
Dear Applied Statistics Workshop,
Please note, there has been a scheduling change. Kosuke Imai, Department of
Politics, Princeton University, will be presenting on November 12th.
In Kosuke's place, this wednesday, October 22nd, Don Rubin, Professor of
Statistics, Harvard University, will present his paper, "For Objective
Causal Inference, Design Trumps Analysis". Don provided the following
abstract,
For obtaining causal inferences that are objective, and therefore have the
best chance of revealing scientific truths, carefully designed and executed
randomized experiments are generally considered to be the gold
standard. Observational
studies, in contrast, are generally fraught with problems that compromise
any claim for objectivity of the resulting causal inferences. The thesis
here is that observational studies have to be carefully designed to
approximate randomized experiments, in particular, without examining any
final outcome data. Often a candidate data set will have to be rejected as
inadequate because of lack of data on key covariates, or because of lack of
overlap
in the distributions of key covariates between treatment and control groups,
often revealed by careful propensity score analyses. Sometimes the template
for the approximating randomized experiment will have to be altered, and the
use of principal stratification can be helpful in doing this. These issues
are discussed and illustrated using the framework of potential outcomes to
define causal effects, which greatly clarifies critical issues.
Don has provided the full paper available here:
http://people.fas.harvard.edu/~jgrimmer/Rubin2008.pdf
The applied statistics workshop meets at 12 noon in Room K-354, CGIS Knafel
(1737 Cambridge St), with a light lunch. Our presentations begin at 1215 and
usually conclude around 130 pm. As always, everyone is welcome!
Cheers
Justin Grimmer
Dear Applied Statistics Community,
Please join us this Wednesday (October 15th) when Stephen Ansolabehere,
Professor in Harvard's Department of Government, will present his work on
"Vote Validation in the 2006 CCES". Stephen provided the following
abstract,
New technology and recent political reform have made vote validation an
easier and
more reliable process than it has been in the past. We present a basic
summary of the
vote validation procedure used in the 2006 CCES, a Web-based survey of
nearly 35,000
Americans that has been validated electronically with new state-wide voter
files. As
the validation method in the CCES is quite different from the method used by
the
National Election Studies (NES) in the 1960s through 1980s, we compare the
CCES
procedure and results with the most recent midterm elections validated by
the NES.
We show that while the rate of vote misreporting is substantially higher in
the 2006
Web-based survey, the pattern of misreporting is consistent with the NES
samples. We
also show how the large sample size in the CCES can be exploited to study
phenomena
beyond vote misreporting using the validated records.
A paper is available for download here (
http://people.fas.harvard.edu/~jgrimmer/AnsolabeherePaper.pdf)
The applied statistics workshop meets at 12 noon in Room K-354, CGIS Knafel
(1737 Cambridge St), with a light lunch. Our presentations begin at 1215
and usually conclude around 130 pm. As always, everyone is welcome!
Cheers
Justin Grimmer
Dear Applied Statistics Community,
Please join us this Wednesday, October 8th when Stefano Iacus, Department
of Economics, Business and Statistics, University of Milan (yes, in Italy)
will be presenting his work on Stochastic differential equations and applied
statistics. Stefano provided the following abstract:
Stochastic differential equations (SDEs) arise naturally in many fields of
science. Solutions of SDEs are continuous time processes and are usually
proposed as alternative models to standard time series. While continuous
time modeling seems better in describing the natural evolving nature of the
underlying data generating process, observations always come in discrete
form. This discrepancy raised new statistical challenges (e.g., the discrete
time likelihood is not always available).
In the first part of the talk, we present few examples (from biostatistics,
econometrics, political analysis, etc.) in which SDEs naturally emerge.
Then, we present the general statistical issues peculiar to these models
and finally we present some new applications (with solutions) like change
point analysis, hypotheses testing and cluster analysis for discretely
observed stochastic differential equations.
Stefano suggested that the following papers might offer helpful background
information for his presentation.
De Gregorio, A., Iacus, S.M. (2008) Clustering of discretely observed
diffusion processes http://arxiv.org/abs/0809.3902
De Gregorio, A., Iacus, S.M. (2008) Divergences Test Statistics for
Discretely Observed Diffusion Processes http://arxiv.org/abs/0808.0853
The workshop will begin at 12 noon in room K-354 in 1737 Cambrdge St
(CGIS-Knafel) with a light lunch and the presentation will commence around
1215. The workshop usually adjourns around 130 pm. All are welcome!
Please contact me with any questions
Justin Grimmer
