Michael Guggisberg


I analyze properties of misspecified discrete choice models and the efficacy of Huber-White (sometimes called ‘robust’) standard errors. The Huber-White correction provides asymptotically correct standard errors for a consistent estimator from a misspecified model. There is little justification for using Huber-White standard errors in discrete choice models since misspecification usually leads to inconsistent estimators. I derive necessary and sufficient conditions for consistency of the maximum likelihood estimator of any potentially misspecified random utility model (e.g. conditional logit). I also derive (easily satisfied) sufficient conditions for consistent estimation of the sign of the data generating parameter. It follows the researcher can consistently test the sign (or nullity) of the parameter from the data generating process using the (possibly) misspecified conditional logit. I investigate small sample properties of the Huber-White estimator via a simulation study and find the correction provides little to no improvement for inferences. This paper can be viewed here.

In this paper I investigate if United States Supreme Court Justices recuse themselves strategically. A strategic recusal is when a Supreme Court justice might fail to remove themself from a case despite having a conflict of interest with the case. I create a new structural model for the Supreme Court recusal process. This is the first paper to use a structural approach to investigate strategic recusals. Using the model, I find evidence justices do recuse themselves strategically or perform some sort of `vote-switching' after a recusal. The evidence provided by this paper agrees with previous research. In a counterfactual simulation, under certain assumptions, I find that in a given year, at most 48% of cases have a justice who remains on the case despite having a conflict of interest. This paper can be viewed here.

Curriculum Vitae


"A Bayesian Approach to Multiple-Output Quantile Regression", Under Review

"On The Misspecified Conditional Logit and Huber-White Standard Error", Journal of Econometric Methods, 2018.

"Strategic Recusals at the United States Supreme Court", Working Paper

This paper proposes a Bayesian approach to Multiple-Output Quantile Regression defined in Hallin et al. (2010). I prove consistency of the posterior and discuss interpretations of the prior. I apply the model to the Tennessee Project STAR experiment and find there is joint increase in the fixed-τ quantile regions for reading and math scores when there is a decrease in the number of students per teacher. This result is consistent with, and much stronger than, the result one would find with multivariate linear multiple regression.  This paper can be viewed here.