gov3009-l October 2012

gov3009-l@lists.fas.harvard.edu

1 participants
6 discussions

Applied Statistics Workshop Cancellation

by Konstantin Kashin

Hello all, Unfortunately, we are canceling this week's meeting of the Applied Statistics Workshop due to the storm and travel cancellations for our speaker. I hope everyone stays safe, and see you next week! -Konstantin -- Konstantin Kashin Ph.D. Candidate in Government Harvard University Mobile: 978-844-0538 E-mail: kkashin(a)fas.harvard.edu Site: http://www.konstantinkashin.com/<http://people.fas.harvard.edu/%7Ekkashin/>

11 years, 6 months

Applied Statistics Workshop: Chad Hazlett on Wed., October 24

by Konstantin Kashin

Dear all, Please join us this Wednesday, October 24, 2012 for the Applied Statistics Workshop. Chad Hazlett, a Ph.D. student from the Department of Political Science at MIT, will give a presentation entitled "Kernel Regularized Least Squares: Moving Beyond Linearity and Additivity Without Sacrificing Interpretability" (this is joint work with Jens Hainmueller from MIT). A light lunch will be served at 12 pm and the talk will begin at 12.15. Abstract: We propose the use of Kernel Regularized Least Squares (KRLS) for social science modeling and inference problems. KRLS borrows from machine learning methods designed to solve regression and classification problems without relying on linearity or additivity assumptions. The method constructs a flexible hypothesis space that uses kernels as radial basis functions and finds the best fitting surface in this space by minimizing a complexity-penalized least squares problem. We provide an accessible explanation of the method and argue that it is well suited for social science inquiry because it avoids strong parametric assumptions and still allows for simple interpretation in ways analogous to OLS or other members of the GLM family. We also extend the method in several directions to make it more effective for social inquiry. In particular, we (1) derive new estimators for the pointwise marginal effects and their variances, (2) establish unbiasedness, consistency, and asymptotic normality of the KRLS estimator under fairly general conditions, (3) develop an automated approach to chose smoothing parameters, and (4) provide companion software. We illustrate the use of the methods through several simulations and a real-data example. An up-to-date schedule for the workshop is available at http://www.iq.harvard.edu/events/node/1208. ~Konstantin -- Konstantin Kashin Ph.D. Candidate in Government Harvard University Mobile: 978-844-0538 E-mail: kkashin(a)fas.harvard.edu Site: http://www.konstantinkashin.com/<http://people.fas.harvard.edu/%7Ekkashin/>

11 years, 6 months

Annual Government Department Poster Session

by Konstantin Kashin

Hi all, Please see the following announcement from Dustin Tingley regarding the Government Department's annual poster session. Dear Colleagues > > The 2nd Annual Harvard Government Department poster session is this > Tuesday at 4pm in the CGIS Cafe. The collection of posters is at > http://projects.iq.harvard.edu/govposters/pages/2012-posters. Last year > the turnout was extremely high. Turnout by the community makes this event. > Great libations served alongside stimulating conversation. > best, > Dustin Tingley -- Konstantin Kashin Ph.D. Candidate in Government Harvard University Mobile: 978-844-0538 E-mail: kkashin(a)fas.harvard.edu Site: http://www.konstantinkashin.com/<http://people.fas.harvard.edu/%7Ekkashin/>

11 years, 6 months

Applied Statistics Workshop: James Scanlan on Wed., October 17

by Konstantin Kashin

Dear all, Please join us for the Applied Statistics Workshop (Gov 3009) this Wednesday, October 17 from 12.00 - 1.30 pm in CGIS Knafel Room 354. James Scanlan <http://www.jpscanlan.com/homepage.html>, an Attorney at Law, will give a presentation entitled "The Mismeasure of Group Differences in the Law and the Social and Medical Sciences". As always, a light lunch will be provided. Abstract: This paper addresses the problematic nature of efforts in the law and the > social and medical sciences to appraise the comparative circumstances of > advantaged and disadvantaged groups on the basis of standard measures of > differences in outcome rates, given that such measures tend to be > systematically affected by the prevalence of an outcome. The rarer an > outcome the greater tends to be the relative difference in experiencing it > and the smaller tends to be the relative difference in avoiding it. Thus, > for example, as mortality declines relative differences in mortality of > advantaged and disadvantaged groups tend to increase while relative > differences in survival tend to decrease; as procedures like immunization > and cancer screening become more common, relative differences in rates of > receipt of those procedures tend to decrease while relative differences in > rates of failing to receive them tend to increase; relaxing mortgage > lending criteria tends to increase relative differences in mortgage > rejection rates while reducing relative differences in mortgage approval > rates. Similarly, among subpopulations where adverse outcomes are > comparatively rare (e.g., persons with high education or high income, > British civil servants), relative differences in adverse outcomes tend to > be larger, while relative differences in favorable outcomes tend to be > smaller, than among subpopulations where adverse outcome are more common. > Absolute differences between outcome rates and differences measured by odds > ratios are unaffected by whether one examines the favorable or the adverse > outcome. But such measures tend also to be affected by the overall > prevalence of an outcome, though in a more complicated way than the two > relative differences. Broadly, as uncommon outcomes become more common > absolute differences tend to increase; as already common outcomes become > even more common, absolute differences tend to decrease. Differences > measured by odds ratios tend to change in the opposite direction of > absolute differences as the prevalence of an outcome changes. The paper > will explain these patterns and the misinterpretations of data on group > differences arising from the failure to understand them. It will also > describe a method for appraising the size of the difference in > circumstances reflected by outcome rates of advantaged and disadvantaged > groups that is theoretically unaffected by the prevalence of the outcome. An up-to-date schedule for the workshop is available at http://www.iq.harvard.edu/events/node/1208. Best, Konstantin -- Konstantin Kashin Ph.D. Candidate in Government Harvard University Mobile: 978-844-0538 E-mail: kkashin(a)fas.harvard.edu Site: http://www.konstantinkashin.com/<http://people.fas.harvard.edu/%7Ekkashin/>

11 years, 6 months

Applied Statistics Workshop: Thales Teixeira on Wed., Oct. 10

by Konstantin Kashin

*Dear all, Please join us for the Applied Statistics Workshop (Gov 3009) this Wednesday, October 10 from 12.00 - 1.30 pm in CGIS Knafel Room 354. Thales Teixeira <http://www.hbs.edu/faculty/Pages/profile.aspx?facId=522373>, Assistant Professor of Business Administration at the Harvard Business School, will give a presentation entitled "Viral Video Advertising". As always, a light lunch will be provided. Abstract:* To become viral, online video ads need to be viewed and then shared. Yet, > what works for one decision may not work for the other. In this research we > propose a novel consumer-centric model of viral advertising consisting of > viewing and sharing decisions. We apply the model to assess the role of > humor, present in 91% of viral ads, by teasing out the differential impact > that type of humor (pure or shocking) has on each decision. In the lab, we > record the facial expressions of consumers as they watch online ads > containing either pure (i.e., smile, laughter) or shocking humor (e.g., > shock from profanity), and examine its impact on their decisions. The video > data is processed using face tracking software and used to calibrate a > dynamic sequential model that accounts for both within and cross-decision > dynamics. We find that shocking humor increases viewing but reduces sharing > compared to no humor at all. Yet, content isn't the only factor of viral ad > success; individual traits also matter. We also find that highly > extraverted and self-directed consumers share humor ads more often and to a > broader group of people each time. The magnitude of the effects of these > two novel findings is then measured in a viral field study in which we > selectively sent ads to participants and tracked views derived from > sharing. We find that extraverted people garnered 300% more total views by > sharing non-shocking humor ads than introverted people sharing ads low in > humor. ** An up-to-date schedule for the workshop is available at http://www.iq.harvard.edu/events/node/1208. Best, Konstantin -- Konstantin Kashin Ph.D. Candidate in Government Harvard University Mobile: 978-844-0538 E-mail: kkashin(a)fas.harvard.edu Site: http://www.konstantinkashin.com/<http://people.fas.harvard.edu/%7Ekkashin/>

11 years, 6 months

Applied Statistics Workshop: Maximilian Kasy on Wed., Oct. 3

by Konstantin Kashin

*Dear all, Please join us for the Applied Statistics Workshop (Gov 3009) this Wednesday, October 3 from 12.00 - 1.30 pm in CGIS Knafel Room 354. Maximilian Kasy <http://scholar.harvard.edu/kasy/>, Assistant Professor of Economics from the Department of Economics at Harvard University, will give a presentation entitled "Identification in General Triangular Systems". As always, a light lunch will be provided. Abstract: This paper discusses identification in continuous triangular systems > without restrictions on heterogeneity or functional form. In particular, we > do not assume separability of structural functions, restrictions on the > dimensionality of unobservables, or monotonicity in unobservables. We do > maintain monotonicity of the first stage relationship in the instrument. We > show that under this condition alone, and given rich enough support of the > data, we can achieve point identification of potential outcome > distributions, and in particular of the average structural function. If the > support of the continuous instrument is not large enough potential outcome > distributions are partially identified. If the instrument is discrete > identification fails completely. The setup discussed in this paper covers > important cases not covered by existing approaches such as conditional > moment restrictions (c.f. Newey and Powell, 2003) and control variables > (c.f. Imbens and Newey, 2009). It covers, in particular, random coe > fficient models, as well as models arising as the reduced form of a system > of structural equations. An up-to-date schedule for the workshop is available at http://www.iq.harvard.edu/events/node/1208. Best, Konstantin* -- Konstantin Kashin Ph.D. Candidate in Government Harvard University Mobile: 978-844-0538 E-mail: kkashin(a)fas.harvard.edu Site: http://www.konstantinkashin.com/<http://people.fas.harvard.edu/%7Ekkashin/>

11 years, 6 months

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gov3009-l October 2012