About

Elie Tamer is an economist whose research spans econometric theory, applied econometrics, and empirical industrial organization. He has coedited prominent journals such as Econometrica, Quantitative Economics, and the Journal of Econometrics. Tamer is a fellow of the Econometric Society and a member of the American Academy of Arts and Sciences, currently serving as the chair of Harvard's Department of Economics.

Research topics

• Computer Science
• Political Science
• Economics
• Mathematical economics
• Econometrics
• Data science

Selected publications

• Prediction Sets and Conformal Inference with Interval Outcomes

  arXiv (Cornell University) · 2025-01-17

  preprintOpen accessSenior author

  Given data on a random variable \(Y\), a prediction set with miscoverage level \(α\in (0,1)\) is a set that contains a new draw of \(Y\) with probability \(1-α\). Among all prediction sets satisfying this coverage property, the oracle prediction set is the one with minimal volume. The oracle prediction set offers a complementary view of the distribution of \(Y\), beyond point estimators such as the mean and quantiles, and has attracted considerable interest recently. This paper develops methods for estimating such prediction sets conditional on observed covariates when \(Y\) is \textit{censored} or \textit{interval-valued}. We characterise the oracle prediction set under partial identification induced by interval censoring and propose consistent estimators for both oracle prediction intervals and more general oracle prediction sets consisting of multiple disjoint intervals. In addition, we apply conformal inference to construct finite-sample valid prediction sets for interval outcomes that remain consistent as the sample size grows, using a conformity score tailored to interval data. The proposed procedure accounts for irreducible prediction uncertainty due to the stochastic nature of outcomes, modelling uncertainty arising from partial identification, and sampling uncertainty that vanishes as sample size increases. We conduct Monte Carlo simulations and two empirical applications using UK job postings data and the US Current Population Survey. The results demonstrate the robustness and efficiency of the proposed methods.

  PublisherOA PDFDOI

• Counterfactual Analysis in Empirical Games

  arXiv (Cornell University) · 2024-10-16

  preprintOpen accessSenior author

  We address counterfactual analysis in empirical models of games with partially identified parameters, and multiple equilibria and/or randomized strategies, by constructing and analyzing the counterfactual predictive distribution set (CPDS). This framework accommodates various outcomes of interest, including behavioral and welfare outcomes. It allows a variety of changes to the environment to generate the counterfactual, including modifications of the utility functions, the distribution of utility determinants, the number of decision makers, and the solution concept. We use a Bayesian approach to summarize statistical uncertainty. We establish conditions under which the population CPDS is sharp from the point of view of identification. We also establish conditions under which the posterior CPDS is consistent if the posterior distribution for the underlying model parameter is consistent. Consequently, our results can be employed to conduct counterfactual analysis after a preliminary step of identifying and estimating the underlying model parameter based on the existing literature. Our consistency results involve the development of a new general theory for Bayesian consistency of posterior distributions for mappings of sets. Although we primarily focus on a model of a strategic game, our approach is applicable to other structural models with similar features.

  PublisherOA PDFDOI

• Heterogeneous Treatment Effects via Linear Dynamic Panel Data Models

  arXiv (Cornell University) · 2024-10-24 · 1 citations

  preprintOpen access

  We study the identification of heterogeneous, intertemporal treatment effects (TE) when potential outcomes depend on past treatments. First, applying a dynamic panel data model to observed outcomes, we show that an instrumental variable (IV) version of the estimand in Arellano and Bond (1991) recovers a non-convex (negatively weighted) aggregate of TE plus non-vanishing trends. We then provide conditions on sequential exchangeability (SE) of treatment and on TE heterogeneity that reduce such an IV estimand to a convex (positively weighted) aggregate of TE. Second, even when SE is generically violated, such estimands identify causal parameters when potential outcomes are generated by dynamic panel data models with some homogeneity or mild selection assumptions. Finally, we motivate SE and compare it with parallel trends (PT) in various settings with experimental data (when treatments are sequentially randomized) and observational data (when treatments are dynamic, rational choices under learning).

  PublisherOA PDFDOI

• Inference on High Dimensional Selective Labeling Models

  arXiv (Cornell University) · 2024-10-24

  preprintOpen access

  A class of simultaneous equation models arise in the many domains where observed binary outcomes are themselves a consequence of the existing choices of of one of the agents in the model. These models are gaining increasing interest in the computer science and machine learning literatures where they refer the potentially endogenous sample selection as the {\em selective labels} problem. Empirical settings for such models arise in fields as diverse as criminal justice, health care, and insurance. For important recent work in this area, see for example Lakkaruju et al. (2017), Kleinberg et al. (2018), and Coston et al.(2021) where the authors focus on judicial bail decisions, and where one observes the outcome of whether a defendant filed to return for their court appearance only if the judge in the case decides to release the defendant on bail. Identifying and estimating such models can be computationally challenging for two reasons. One is the nonconcavity of the bivariate likelihood function, and the other is the large number of covariates in each equation. Despite these challenges, in this paper we propose a novel distribution free estimation procedure that is computationally friendly in many covariates settings. The new method combines the semiparametric batched gradient descent algorithm introduced in Khan et al.(2023) with a novel sorting algorithms incorporated to control for selection bias. Asymptotic properties of the new procedure are established under increasing dimension conditions in both equations, and its finite sample properties are explored through a simulation study and an application using judicial bail data.

  PublisherOA PDFDOI

• Estimating high dimensional monotone index models by iterative convex optimization

  Journal of Econometrics · 2024-12-02

  preprintOpen access
  PublisherOA PDFDOI

• Five decades of the Journal of Econometrics: An activity report

  Journal of Econometrics · 2023-02-06 · 7 citations

  articleSenior author
  PublisherDOI

• MCMC confidence sets for identified sets

  2023-11-02 · 9 citations

  preprintOpen accessSenior author

  In complicated/nonlinear parametric models, it is generally hard to determine whether the model parameters are (globally) point identified. We provide computationally attractive procedures to construct confidence sets (CSs) for identified sets of parameters in econometric models defined through a likelihood or a vector of moments. The CSs for the identified set or for a function of the identified set (such as a subvector) are based on inverting an optimal sample criterion (such as likelihood or continuously updated GMM), where the cutoff values are computed via Monte Carlo simulations directly from a quasi posterior distribution of the criterion. We establish new Bernstein-von Mises type theorems for the posterior distributions of the quasi-likelihood ratio (QLR) and profile QLR statistics in partially identified models, allowing for singularities. These results imply that the Monte Carlo criterion-based CSs have correct frequentist coverage for the identified set as the sample size increases, and that they coincide with Bayesian credible sets based on inverting a LR statistic for point-identified likelihood models. We also show that our Monte Carlo optimal criterion-based CSs are uniformly valid over a class of data generating processes that include both partially-and pointidentified models. We demonstrate good finite sample coverage properties of our proposed methods in four non-trivial simulation experiments: missing data, entry game with correlated payoff shocks, Euler equation and finite mixture models. Finally, our proposed procedures are applied in two empirical examples.

  PublisherOA PDFDOI

• Efficient estimation of average derivatives in NPIV models: Simulation comparisons of neural network estimators

  Journal of Econometrics · 2023-03-04 · 8 citations

  articleSenior author
  PublisherDOI

• Recent Developments in Partial Identification

  Annual Review of Economics · 2023-09-13 · 10 citations

  articleOpen accessSenior author

  Identification strategies concern what can be learned about the value of a parameter based on the data and the model assumptions. The literature on partial identification is motivated by the fact that it is not possible to learn the exact value of the parameter for many empirically relevant cases. A typical result in the literature on partial identification is a statement about characterizing the identified set, which summarizes what can be learned about the parameter of interest given the data and model assumptions. For instance, this may mean that the value of the parameter can be learned to be necessarily within some set of values. First, the review surveys the general frameworks that have been developed for conducting a partial identification analysis. Second, the review surveys some of the more recent results on partial identification.

  PublisherOA PDFDOI

• Identification of dynamic binary response models

  Journal of Econometrics · 2023-09-15 · 12 citations

  articleSenior author
  PublisherDOI

Recent grants

• CAREER: Robust Inference in Incomplete Econometric Models

  NSF · $400k · 2004–2010

• Identification and Inference in Some Econometrics Models

  NSF · $235k · 2009–2013

Frequent coauthors

• Shakeeb Khan

  25 shared

• Áureo de Paula

  19 shared

• Ariel Pakes

  19 shared

• Brendan Kline

  The University of Texas at Austin

  14 shared

• Adam Rosen

  13 shared

• Andrés Aradillas-López

  Pennsylvania State University

  11 shared

• Fu Ouyang

  University of Queensland

  10 shared

• Eleni Aristodemou

  University of Cyprus

  9 shared

Awards & honors

• Fellow of the Econometric Society