Dear Workshop Community,
Our next meeting will be Wednesday October 9, where Max Goplerud will present his job
market paper "Modelling Heterogeneity Using Bayesian Structured Sparsity”.
Abstract: How to estimate heterogeneity, e.g. the effect of some variable differing across
observations, is a key question in political science. Methods for doing so make
simplifying assumptions about the underlying nature of the heterogeneity to draw reliable
inferences. This paper allows a common way of simplifying complex phenomenon (placing
observations with similar effects into discrete groups) to be integrated into regression
analysis. The framework allows researchers to (i) use their prior knowledge to guide which
groups are permissible and (ii) appropriately quantify uncertainty. The paper does this by
translating work on "structured sparsity" from a penalized likelihood approach
into a Bayesian prior and deriving theoretical results on posterior propriety and
inference. It shows that this method outperforms state-of-the-art methods for estimating
heterogeneous effects when the underlying heterogeneity is grouped and more effectively
identifies groups of observations with different effects in observational data. A link to
the paper can be found at j.mp/goplerud_sparsity <http://j.mp/goplerud_sparsity>.
Where: CGIS Knafel Building, Room K354 (see this link
<https://map.harvard.edu/?bld=04471&level=9> for directions).
When: Wednesday, October 9 at 12 noon - 1:30 pm.
All are welcome! Lunch will be provided.
Best,
Georgie
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