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| 1999 | Diplom-Volkswirt, University of Cologne | | 2000 | M.Sc., Economics and Philosophy (with Distinction), LSE, London, England, UK | | 2001 | M.A., Economics, Northwestern University, Evanston, IL | | 2005 | PhD, Economics, Northwestern University, Evanston, IL | | 2005 - 2010 | Assistant Professor, New York University, NY, USA | | 2009 | Visiting Research Fellow, Cowles Foundation, Yale University, New Haven, CT, USA | | Since 2010 | Associate Professor, Cornell University, Ithaca, NY, USA (on leave, July 2016 - present) | | Since 2016 | Professor, University of Bonn |
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In past research, Stoye explored connections between statistical decision theory and applied economic analysis, with special attention to treatment/policy choice problems and to minimax regret as optimality criterion. In axiomatic analyses, he provided normative foundations for minimax regret and related criteria [6,7,8]. Parallel work in econometrics undertook finite sample (non-approximate) analysis of econometric treatment choice problems, e.g. the use of covariates in treatment assignment [9,10,11]. Stoye also advanced the literature on partial (set valued) identifiability of parameters and on inference in such settings [12,13]. More recently (with Stefan Hoderlein, Boston College), he proposed statistical tests of revealed preference analysis from microeconomic theory, asking whether the homo oeconomicus model is testable under otherwise weak assumptions on real-world data [1,2].
Some of Stoye’s current research extends this last project. With Yuichi Kitamura (Yale), he identifies the precise empirical content of Random Utility models in repeated cross-section data, assuming unrestricted unobservable heterogeneity among consumers and therefore an infinite dimensional nuisance parameter. Their statistical test overcomes both computational and theoretical (in the form of nonstandard asymptotic behavior) hurdles. It is extended to other, less standard economics models in work with Kitamura, Rahul Deb (Toronto), and John Quah (Johns Hopkins).The long-term vision is to fundamentally rethink the large economics literature on nonparametric demand, complementing its current “specific to general” approach (i.e., imposing an extremely tight structure and maybe gradually relaxing it) with a “general to specific” approach that initially tests whether data are consistent with minimal economic assumptions and, in future research, gradually relaxes the generality to obtain tighter conclusions. In other early stage research, Stoye (with Hiroaki Kaido, Boston University, and Francesca Molinari, Cornell) develops confidence sets for the optimal values of programs with estimated objective function as well as constraints. These sets will be valid uniformly over a large class of sampling processes and without so-called constraint qualifications. Notable applications are to policy counterfactuals in economic models as well as to projections of partially identified parameter vectors and are being explored empirically.
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Research Area H
My recent focus has been on two issues: (i) What discipline do core economic assumptions, e.g. homo oeconomicus, directly impose on data that are realistically observed if one drops auxiliary assumptions of convenience? For example, in the empirically relevant context of repeated cross-sectional data, this means to assume that all individuals in an underlying population are rational in the sense of maximizing some criterion function, but that the population distribution of criterion functions is unrestricted. This leads to nonparametric testing of extremely high dimensional models, though important dimension reductions turn out to be available in practice [1,2,3]. Extensions to less standard models of consumer behavior are in progress. (ii) How can we perform inference on low-dimensional functions of moderate to high dimensional parameters that are only partially statistically identified. Applications include separate inference on components of partially identified vectors but also value functions of optimization problems (e.g., maximization of social welfare in macroeconomic models) with estimated objective function and constraints. The inference problem is highly irregular due to the presence of nonidentifiable nuisance parameters. The solution involves powerful new regularization techniques as well as novel black-box optimization algorithms whose convergence is established [4,5]. |
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[ 1] Stefan Hoderlein, Jörg Stoye
Testing stochastic rationality and predicting stochastic demand: the case of two goods
Econ. Theory Bull. , 3: (2): 313--328
2015
DOI: 10.1007/s40505-014-0061-5[ 2] Stefan Hoderlein, Jörg Stoye
Revealed Preferences in a Heterogeneous Population
The Review of Economics and Statistics , 96: (2): 197--213
2014[ 3] Yuichi Kitamura, Jörg Stoye
Nonparametric Analysis of Random Utility Models
eprint arXiv:1606.04819 Revise and Resubmit at Econometrica
2016[ 4] Hiroaki Kaido, Francesca Molinari, Jörg Stoye
Confidence Intervals for Projections of Partially Identified Parameters
eprint arXiv:1601.00934 Revise and Resubmit at Econometrica
2016[ 5] Hiroaki Kaido, Francesca Molinari, Jörg Stoye, Matthew Thirkettle
Calibrated Projection in MATLAB: Users' Manual
eprint arXiv:1710.09707
2017[ 6] Jörg Stoye
Choice theory when agents can randomize
J. Econom. Theory , 155: : 131--151
2015
DOI: 10.1016/j.jet.2014.11.011[ 7] Jörg Stoye
Axioms for minimax regret choice correspondences
J. Econom. Theory , 146: (6): 2226--2251
2011
DOI: 10.1016/j.jet.2011.10.004[ 8] Jörg Stoye
Statistical decisions under ambiguity
Theory and Decision , 70: (2): 129--148
2011
DOI: 10.1007/s11238-010-9227-2[ 9] Jörg Stoye
Minimax regret treatment choice with covariates or with limited validity of experiments
J. Econometrics , 166: (1): 138--156
2012
DOI: 10.1016/j.jeconom.2011.06.012[ 10] Jörg Stoye
Minimax regret treatment choice with finite samples
J. Econometrics , 151: (1): 70--81
2009
DOI: 10.1016/j.jeconom.2009.02.013[ 11] Jörg Stoye
Minimax regret treatment choice with incomplete data and many treatments
Econometric Theory , 23: (1): 190--199
2007
DOI: 10.1017/S0266466607070089[ 12] Jörg Stoye
Partial identification of spread parameters
Quant. Econ. , 1: (2): 323--357
2010
DOI: 10.3982/QE24[ 13] Jörg Stoye
More on confidence intervals for partially identified parameters
Econometrica , 77: (4): 1299--1315
2009
DOI: 10.3982/ECTA7347 |
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• Review of Economics and Statistics (since 2014)
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