Goplerud, Max. 2015. "The First Time is (Mostly) the Charm: Special Advisers as Parliamentary Candidates and Members of Parliament." Parliamentary Affairs. 68(2):332-351. [publisher's version]
Goplerud, Max. 2014. "Appendix 1: Methodology" and "Appendix 2: Further Work on the Distribution and Tenure of Special Advisers" in Special Advisers: Who They Are, What They Do and Why They Matter, (eds.) Ben Yong and Robert Hazell. Oxford: Hart. [data on special advisers, 1979-2013]
My working papers are available below.
Goplerud, Max. "Modelling Heterogeneity Using Bayesian Structured Sparsity." [draft]
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.
Goplerud, Max. "Fast and Accurate Estimation of Non-Nested Binomial Hierarchical Models Using Variational Inference." [draft][R package]
Estimating non-linear hierarchical models can be computationally burdensome in the presence of large datasets and many non-nested random effects. Popular inferential techniques may take hours to fit even relatively straightforward models. This paper provides two contributions to scalable and accurate inference. First, I propose a new mean-field algorithm for estimating logistic hierarchical models with an arbitrary number of non-nested random effects. Second, I propose ``marginally augmented variational Bayes'' (MAVB) that further improves the initial approximation through a post-processing step. I show that MAVB provides a guaranteed improvement in the approximation quality at low computational cost and induces dependencies that were assumed away by the initial factorization assumptions. I apply these techniques to a study of voter behavior. Existing estimation took hours whereas the algorithms proposed run in minutes. The posterior means are well-recovered even under strong factorization assumptions. Applying MAVB further improves the approximation by partially correcting the under-estimated variance. The proposed methodology is implemented in an open source software package.
Goplerud, Max and Daniel M. Smith. "Who Answers for the Government? Bureaucrats, Ministers, and Responsible Parties" [draft]
A key feature of parliamentary democracy is government accountability vis-à-vis the legislature, but the important question of who speaks for the government—cabinet ministers or unelected bureaucrats, and the institutional underpinnings of this behavior—receives scant attention in the existing literature. We investigate this question with the case of Japan, and data on millions of committee speeches spanning distinct electoral and legislative institutional environments. We document how a party-strengthening electoral system reform in 1994 facilitated a dramatic shift in the nature of government accountability to parliament: speeches by ministers increased, speeches by bureaucrats decreased, and discursive accountability between ministers and opposition legislators increased. Subsequent legislative reforms expanding junior ministerial roles and placing explicit limits on bureaucratic participation further reinforced the effects. These findings shed new light on the institutional foundations of responsible party government in general, as well as its progressive development in Japan.
Goplerud, Max, Shiro Kuriwaki, Marc Ratkovic, and Dustin Tingley. "Sparse Multilevel Regression (and Poststratification (sMRP))." [draft]
Multilevel models have long played an important role in a variety of social sciences. We extend this framework by bring to bear recent developments in the machine learning literature to allow for considerable flexibility. We introduce a sparse regression framework that covers both the linear case as well as a logit model for binary outcome data. We leverage recent computational tricks based on data-augmentation to dramatically speed up estimation times with equal or better performance compared to existing approaches. We apply our model in the context of multilevel modelling with post-stratification which has become a common tool for survey researchers.