Martin Fankhauser

My primary research interests lie in econometrics, with a focus on partial identification problems and Bayesian econometrics. I will be on the job market in Fall 2026. 

JOB MARKET PAPER

Sharp Identification for Regressions with Interval-Observed and Missing Covariates (with Peled, G.)

Researchers frequently estimate regressions when key variables are missing or observed only through intervals. Existing approaches typically resolve this incompleteness through imputation or auxiliary assumptions on the incomplete-data process. We instead ask what the maintained regression model and incomplete data alone imply. For a broad class of conditional moment models, we derive an operational characterization of the sharp identified set, develop a Hausdorff-consistent estimator with uniformly valid confidence regions, and provide tractable computational methods for common regression models. An application to a multiple-price-list design shows that incomplete-data-robust inference can remain informative, while also demonstrating that sharpness matters: non-sharp outer sets can substantially overstate the uncertainty implied by the observed data.

WORKING PAPERS

• Fankhauser, M.  – "Robust Bayesian Inference under Partially Specified Priors"
• Fankhauser, M., Marcellino, M & Tornese, T. – "Power Priors for VARs"
• Fankhauser, M. , Przewoski, J. & Nespoli, G. – "Model Confidence Set for Stata"
• Fankhauser, M. – "Conformal Quantile Nowcasting".