26 March 2012
We hope you can join us this Wednesday, March 28, 2012 for the Applied Statistics Workshop. Teppei Yamamoto, Assistant Professor from the Department of Political Science at MIT, will give a presentation entitled "A Multinomial Response Model for Varying Choice Sets, with Application to Partially Contested Multiparty Elections". A light lunch will be served at 12 pm and the talk will begin at 12.15.
"A Multinomial Response Model for Varying Choice Sets, with Application to Partially Contested Multiparty Elections"
Department of Political Science, MIT
CGIS K354 (1737 Cambridge St.)
Wednesday, March 28th, 2012 12.00 pm
This paper proposes a new multinomial choice model which explicitly takes into account variation in choice sets across observations. The proposed varying choice set logit model relaxes the independence of irrelevant alternatives assumption by allowing the individual random utility function to directly depend on choice set types, and can be applied to a variety of data in which some individuals can only choose from a subset of the theoretically possible responses. Both frequentist and Bayesian simulation-based estimation procedures are developed using the Monte Carlo expectation-maximization algorithm and Markov chain Monte Carlo, respectively. The proposed model can be used to analyze survey data in partially contested multiparty elections in which some political parties do not run their candidates in every district. For illustration, I apply the proposed method to the 1996 Japanese general election, where none of the districts was contested by all of the six major parties.
19 March 2012
We hope you can join us this Wednesday, March 21, 2012 for the Applied Statistics Workshop. David Reshef, an MD/PhD student at the Harvard-MIT Division of Health Sciences and Technology (HST), will give a presentation entitled "Detecting Novel Bivariate Associations in Large Data Sets". A light lunch will be served at 12 pm and the talk will begin at 12.15.
"Detecting Novel Bivariate Associations in Large Data Sets"
Harvard-MIT Division of Health Sciences and Technology
CGIS K354 (1737 Cambridge St.)
Wednesday, March 21st, 2012 12.00 pm
Identifying interesting relationships between pairs of variables in large data sets is increasingly important. One way of doing so is to search such data sets for pairs of variables that are closely associated. This can be done by calculating some measure of dependence for each pair, ranking the pairs by their scores, and examining the top-scoring pairs. We outline two heuristic properties--generality and equitability--that the statistic we use to measure dependence should have in order for such a strategy to be effective. We present a measure of dependence for two-variable relationships, the maximal information coefficient (MIC), that has these properties. MIC captures a wide range of associations both functional and not (generality), and assigns similar scores to relationships with similar noise levels, regardless of relationship type (equitability). Finally, we show that MIC belongs to a larger class of maximal information-based nonparametric exploration (MINE) statistics for identifying and classifying relationships.
6 March 2012
Every discovery of a plausible instrumental variable sparks a cottage industry of papers all using the same instrument to ask different questions. A working paper by Heather Sarsons, titled "Rainfall and Conflict" calls one of these cottage industries into serious question. From the abstract:
Starting with Miguel, Satyanath, and Sergenti (2004), a large literature has used rainfall variation as an instrument to study the impacts of income shocks on civil war and conflict. These studies argue that in agriculturally-dependent regions, negative rain shocks lower income levels, which in turn incites violence. This identi cation strategy relies on the assumption that rainfall shocks affect conflict only through their impacts on income. I evaluate this exclusion restriction by identifying districts that are downstream from dams in India. In downstream districts, income is much less sensitive to rainfall fluctuations. However, rain shocks remain equally strong predictors of riot incidence in these districts. These results suggest that rainfall affects rioting through a channel other than income and cast doubt on the conclusion that income shocks incite riots.
It's a short, readable paper -- worth checking out if you're into this kind of thing.
5 March 2012
We hope you can join us this Wednesday, March 7, 2012 for the Applied Statistics Workshop. Joshua Goodman, Assistant Professor of Public Policy at the Harvard Kennedy School, will give a presentation entitled "Flaking Out: Snowfall, Disruptions of Instructional Time, and Student Achievement". A light lunch will be served at 12 pm and the talk will begin at 12.15.
"Flaking Out: Snowfall, Disruptions of Instructional Time, and Student Achievement"
Harvard Kennedy School
CGIS K354 (1737 Cambridge St.)
Wednesday, March 7th, 2012 12.00 pm
Recent research on charter schools, summer learning loss, and international achievement suggests that instructional time is a critical input to the education production function. Using student and school-grade fixed effects models with data from Massachusetts, I find no relation between school closures and achievement but a strong relation between student absences and achievement. I then confirm these results using temporal and spatial variation in snowfall to provide better identification. Extreme snowfall induces school closures but does not affect achievement. Moderate snowfall induces student absences and does reduce achievement. Instrumental variables estimates suggest that each absence induced by bad weather reduces math achievement by 0.05 standard deviations. These results are consistent with a model of instruction in which coordination of students is the central challenge. Teachers deal well with coordinated disruptions of instructional time like school closures, but deal poorly with absences that affect different students and different times. These estimates suggest that absences are responsible for up to 20% of the achievement gap between poor and nonpoor students. They also suggest that policies designed solely to increase instructional time may not be effective.