Some Papers, etc

Working Paper
Marx, Philip, Elie Tamer, and Xun Tang. Working Paper. “Parallel Trends and Dynamic Choices”. Publisher's Version
Syrgkanis, Vasilis, Elie Tamer, and Juba Ziani. Working Paper. “Inference on Auctions with Weak Assumptions on Information”. Abstract

Given a sample of bids from independent auctions, this paper examines the question of inference on auction objects (like valuation distributions, welfare measures, etc) under weak assumptions on information. We leverage the re- cent contributions of Bergemann and Morris [2013] in the robust mechanism design literature that exploit the link between Bayesian Correlated Equilibria and Bayesian Nash Equilibria in incomplete information games, to construct an econometrics framework that is computationally feasible and robust to assump- tions about information. Checking whether a particular valuation distribution belongs to the identified set is as simple as determining whether a linear program (LP) is feasible. This is the key characteristic of our framework. A similar LP can be used to learn about various welfare measures and policy counterfactuals. For inference and to summarize statistical uncertainty, we propose novel finite sample methods using tail inequalities that are used to construct confidence sets on identified sets. Monte Carlo experiments show adequate finite sample properties. We illustrate our approach by applying our methods to a data set from search Ad auctions and to data from OCS auctions.

Khan, Shakeeb, Maria Ponomareva, and Elie Tamer. Forthcoming. “Identification of Dynamic Binary Response Models - forthcoming, Journal of Econometrics. 2023.”.
Chen, Xiaohong, Elie Tamer, and Alexander Torgovitsky. 2015. “Sensitivity Analysis in Semiparametric Likelihood Models”. Abstract

We provide methods for inference on a finite dimensional parameter of interest,
2 <d , in a semiparametric probability model when an infinite dimensional nuisance parameter, g, is present. We depart from the semiparametric literature in that we do not require that the pair (, g) is point identified and so we construct confidence regions for that are robust to non-point identification. This allows practitioners to examine the sensitivity of their estimates of to specification of g in a likelihood setup. To construct these confidence regions for , we invert a profiled sieve likelihood ratio (LR) statistic. We derive the asymptotic null distribution of this profiled sieve LR, which is nonstandard when is not point identified (but is 2 distributed under point identification). We show that a simple weighted bootstrap procedure consistently estimates this complicated distribution’s quantiles. Monte Carlo studies of a semiparametric dynamic
binary response panel data model indicate that our weighted bootstrap procedures performs adequately in finite samples. We provide three empirical illustrations where we compare our results to the ones obtained using standard (less robust) methods.
Keywords: Sensitivity Analysis, Semiparametric Models, Partial Identification, Irregular Functionals, Sieve Likelihood Ratio, Weighted Bootstrap


Abstract. Randomized controlled trials (RCTs) are routinely used in medicine and are becoming more popular in economics. Data from RCTs are used to learn about treatment effects of interest. This paper studies what one can learn about the average treatment response (ATR) and average treatment effect (ATE) from RCT data under various assumptions and compares that to using observational data. We find that data from an RCT need not point identify the ATR or ATE because of selection into an RCT, as subjects are not randomly assigned from the population of interest to participate in the RCT. This problem relating to external validity is the primary problem we study. So, assuming internal validity of the RCT, we study the identified features of these treatment effects under a variety of weak assumptions such as: mean independence of response from participation, an instrumental variable assumption, or that there is a linear effect of participation on response. In particular we provide assumptions sufficient to point identify the ATR or the ATE from RCT data and also shed light on when the sign of the ATE can be identified. We then characterize assumptions under which RCT data provide more information than observational data.
Keywords: randomized controlled trials, experiments, treatment effect, identification

Kline, Brendan, and Elie Tamer. Working Paper. “The Empirical content of Models with Social Interactions”. Abstract

Empirical models with social interactions or peer effects allow the out- come of an individual to depend on the outcomes, choices, treatments, and/or characteristics of the other individuals in the group. We document the subtle re- lationship between the data and the objects of interest in models with interactions in small groups, and show that some econometric assumptions, that are direct ex- tensions from models of individualistic treatment response, implicitly entail strong behavioral assumptions. We point out two such econometric assumptions, EITR, or empirical individual treatment response, and EGTR, or empirical group treatment response. In some cases EITR and/or EGTR are inconsistent with a class of plau- sible economic models for the interaction under consideration; in other cases these econometric assumptions imply significant assumptions on behavior that are not necessarily implied by economic theory. We illustrate this using relevant examples of interaction in immunization and disease, and in educational achievement. We conclude that it is important for applications in this class of models with small group interactions to recognize the restrictions some assumptions impose on behav- ior.


The linear-in-means model is often used in applied work to empirically study the role of social interactions and peer effects.  We document the subtle relationship between the parameters of the linear-in-means model and the parameters relevant for policy analysis, and study the interpretations of the model under two different scenarios. First, we show that without further assumptions on the model the direct analogs of standard policy relevant parameters are either undefined or are complicated functions not only of the parameters of the linear-in-means model but also the parameters of the distribution of the unobservables.  This complicates the interpretation of the results. Second, and as in the literature on simultaneous equations, we show that it is possible to interpret the parameters of the linear-in-means model under additional assumptions on the social interaction, mainly that this interaction is a result of a particular {\it economic game}.  These assumptions that the game is built on  rule out economically relevant models.   We illustrate this using  examples of social interactions in educational achievement. We conclude that care should be taken when estimating and especially when interpreting coefficients from linear in means models.

Tamer, Elie. 2010. “Partial Identification in Econometrics.” Annual Reviews in Economics, 2, 1, 167-195. Abstract

Identification in econometric models maps prior assumptions and the data to information about a parameter of interest. The partial identification approach to inference recognizes that this process should not result in a binary answer that consists of whether the parameter is point identified. Rather, given the data, the partial identification approach characterizes the informational content of various assumptions by providing a menu of estimates, each based on different sets of assumptions, some of which are plausible and some of which are not. Of course, more assumptions beget more information, so stronger conclusions can be made at the expense of more assumptions. The partial identification approach advocates a more fluid view of identification and hence provides the empirical researcher with methods to help study the spectrum of information that we can harness about a parameter of interest using a menu of assumptions. This approach links conclusions drawn from various empirical models to sets of assumptions made in a transparent way. It allows researchers to examine the informational content of their assumptions and their impacts on the inferences made. Naturally, with finite sample sizes, this approach leads to statistical complications, as one needs to deal with characterizing sampling uncertainty in models that do not point identify a parameter. Therefore, new methods for inference are developed. These methods construct confidence sets for partially identified parameters, and confidence regions for sets of parameters, or identifiable sets.


Journal Article
Khan, Shakeeb, Fu Ouyang, and Elie Tamer. 2021. “Inference on Semiparametric Multinomial Response Models.” Quantitative Economics 12 (3): 743-777.
Ciliberto, Federico, Charles Murry, and Elie Tamer. 2021. “Market Structure and Competition in Airline Markets.” Journal of Political Economy 129 (11): 2995-3038.
ssrn-id2777820.pdf cmt.pdf
Kline, Brendan, and Elie Tamer. 2018. “Identification of Treatment Effects with Selective Participation in a Randomized Trials (forthcoming).” Econometrics Journal 21 (3): 332-353.