Student on the 2021 Job Market


This year we have one student working in the field of Econometrics on the academic job market. Isaac Loh studies identification and estimation in non-parametric instrumental variable (IV) models when both the regressor and the instrument are discrete random variables. His main result shows that even when the instrument is binary, the model is most often point identified via a set of polynomial equations that in turn lead to an estimator. You can find his job market paper here. Additional information can be found on his website: Isaac Loh

Student on the Market

This year we have one student working in the field of Econometrics on the academic job market. Max Tabord-Meehan studies how to optimally stratify in randomized controlled experiments using stratification trees in order to obtain estimators of average treatment effects with small variance. You can find here job market paper here. Additional information can be found on her website: Max Tabord-Meehan

Students on the job market

This year we have two students working in the field of Econometrics on the academic job market.

Vishal Kamat works on identification of program effects in settings with latent choice sets; that is, situations where the unobserved heterogeneity that arises when the choice set from which the agent selects treatment is heterogeneous and unobserved by the researcher. You can find his job market paper here.

 

 

 

Eric Mbakop works on identification in auction models with discrete unobserved heterogeneity and incomplete bid data; that is, settings where the econometrician observes an incomplete set of bids from several auctions and does not observe all the variables that affect the distribution of bidders’ valuation.You can find his job market paper here.