Department of Economics / Cowles Foundation

Alfred Cowles Professor, Department of Economics / Cowles Foundation

Papers and Software

Some Papers 

Counterfactual Analysis for Structural Dynamic Discrete Choice Models  [Supplemental Appendix] (with Myrto Kalouptsidi, Lucas Lima, and Eduardo Souza-Rodrigues, forthcoming, Review of Economic Studies)

Nonparametric Counterfactuals in Random Utility Models (with Jörg Stoye)

Nonparametric Analysis of Finite Mixtures (with Louise Laage)

Revealed Price Preference: Theory and Empirical Analysis (with Rahul Deb, John K.-H. Quah and Jörg Stoye, Review of Economic Studies 90, 707-743, 2023)

A Comment on “On the Informativeness of Descriptive Statistics for Structural Estimates” by Isaiah Andrews, Matthew Gentzkow, and Jesse M. Shapiro  (Econometrica 88, 2265-69, 2020)

Unobserved Heterogeneity in Auctions (with Phil Haile, Econometrics Journal 19, C1-C19, 2019)

Nonparametric Analysis of Random Utility Models [Supplemental Appendix](with Jörg Stoye, Econometrica 86, 1183-1909, 2018)

Using Mixtures in Econometric Models: A Brief Review and Some New Results (with Giovanni Compiani, Econometrics Journal 19, C95-C127, 2016) 

Partial Identification of Finite Mixtures in Econometric Models (with Marc Henry and Bernard Salanié, Quantitative Economics 5, 123-144, 2014)

Robustness, Infinitesimal Neighborhoods, and Moment Conditions [Supplemental Appendix](with Taisuke Otsu and Kirill Evdokimov, Econometrica 81, 1185-1201, 2013)

Nonparametric Estimation in Random Coefficients Binary Choice Models [Supplemental Appendix] (with Eric Gautier, Econometrica 81, 581-607, 2013)

On the Asymptotic Optimality of Empirical Likelihood for Testing Moment Restrictions [Supplemental Appendix] (with Andres Santos and Azeem Shaikh, Econometrica 80, 413-423, 2012)

Robust Estimation of Moment Condition Models with Weakly Dependent Data (with Kirill Evdokimov and Taisuke Otsu, under revision)

Software for Empirical Likelihood


(This is a zip file for MATLAB/STATA codes Kirill Evdokomiv (UPF) and I wrote for implementing empirical likelihood (EL).  They can be used to calculate EL estimators, confidence intervals and test statistics for general models.  We used them for actual examples, and they ran well: see the description file for details and instructions.  We acknowledge support from NSF  grants SES-055127 and SES-0851759.)