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Tamer, Elie

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Tamer

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Elie

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Tamer, Elie
Author's Publications: search.filters.isAuthorOfPublication.3abb6af4-03c2-4712-838f-d927bc5c1e86

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Now showing 1 - 9 of 9
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    Identification of Treatment Effects With Selective Participation in a Randomized Trial
    (Oxford University Press (OUP), 2018-09-14) Kline, Brendan; Tamer, Elie
    Randomized trials (RTs) are used to learn about treatment effects. This paper studies identification of average treatment response (ATR) and average treatment effect (ATE) from RT data under various assumptions. The focus is the problem of external validity of the RT. RT data need not point identify the ATR or ATE because of selective participation in the RT. The paper reports partial‐identification and point‐identification results for the ATR and ATE based on RT data under a variety of assumptions. The results include assumptions sufficient to point identify the ATR or ATE from RT data. Under weaker assumptions, the ATR or ATE is partially identified. Further, attention is given to identification of the sign of the ATE and identification of whether participation in the RT is selective. Finally, identification from RT data is compared to identification from observational data.
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    Identification of Panel Data Models with Endogenous Censoring
    (2015) Khan, S.; Ponomareva, M.; Tamer, Elie
    We study inference on parameters in censored panel data models, where the censoring can depend on both observable and unobservable variables in arbitrary ways. Under some general conditions, we characterize the information the model and data contain about the parameters of interest by deriving the identified sets: Every parameter that belongs to these sets is observationally equivalent to the true parameter - the one that generated the data . We consider two separate sets of assumptions (2 models): the first uses stationarity on the unobserved disturbance terms. The second is a nonstationary model with a conditional independence restriction. Based on the characterizations of the identified sets, we provide a valid inference procedure that is shown to yield correct confidence sets based on inverting stochastic dominance tests. Also, we also show how our results extend to empirically interesting dynamic versions of the model with both lagged observed outcomes, and lagged indicators. We also show extensions to models with factor loads. In addition, and for both models, we provide sufficient conditions for point identification in terms of support conditions.The paper then examines sizes of the identified sets, and a Monte Carlo exercise shows reasonable small sample performance of our procedures.
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    Partial Identification in Econometrics
    (Annual Reviews, 2010) Tamer, Elie
    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.
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    Some Interpretation of the Linear-In-Means Model of Social Interactions
    (2014) Kline, Brendan; Tamer, Elie
    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 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.
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    Quantile Uncorrelation and Instrumental Regressions
    (Walter de Gruyter GmbH, 2012) Komarova, Tatiana; Severini, Thomas A.; Tamer, Elie
    We introduce a notion of median uncorrelation that is a natural extension of mean (linear) uncorrelation. A scalar random variable Y is median uncorrelated with a k-dimensional random vector X if and only if the slope from an LAD regression of Y on X is zero. Using this simple definition, we characterize properties of median uncorrelated random variables, and introduce a notion of multivariate median uncorrelation. We provide measures of median uncorrelation that are similar to the linear correlation coefficient and the coefficient of determination. We also extend this median uncorrelation to other loss functions. As two stage least squares exploits mean uncorrelation between an instrument vector and the error to derive consistent estimators for parameters in linear regressions with endogenous regressors, the main result of this paper shows how a median uncorrelation assumption between an instrument vector and the error can similarly be used to derive consistent estimators in these linear models with endogenous regressors. We also show how median uncorrelation can be used in linear panel models with quantile restrictions and in linear models with measurement errors.
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    Bayesian inference in a class of partially identified models
    (The Econometric Society, 2016) Kline, Brendan; Tamer, Elie
    This paper develops a Bayesian approach to inference in a class of partially identified econometric models. Models in this class are characterized by a known mapping between a point identified reduced-form parameter µ, and the identified set for a partially identified parameter θ. The approach maps posterior inference about µ to various posterior inference statements concerning the identified set for θ, without the specification of a prior for θ. Many posterior inference statements are considered, including the posterior probability that a particular parameter value (or a set of parameter values) is in the identified set. The approach applies also to functions of θ. The paper develops general results on large sample approximations, which illustrate how the posterior probabilities over the identified set are revised by the data, and establishes conditions under which the Bayesian credible sets also are valid frequentist confidence sets. The approach is computationally attractive even in high-dimensional models, in that the approach avoids an exhaustive search over the parameter space. The performance of the approach is illustrated via Monte Carlo experiments and an empirical application to a binary entry game involving airlines.
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    Identification of Preferences in Network Formation Games
    (2015) de Paula, Áureo; Richards-Shunik, Seth; Tamer, Elie
    This paper provides a framework for identifying preferences in a large network under the assumption of pairwise stability of network links. Network data present difficulties for identification, especially when links between nodes in a network can be interdependent: e.g., where indirect connections matter. Given a preference specification, we use the observed proportions of various possible payoff-relevant local network structures to learn about the underlying parameters. We show how one can map the observed proportions of these local structures to sets of parameters that are consistent with the model and the data. Our main result provides necessary conditions for parameters to belong to the identified set, and this result holds for a wide class of models. We also provide sufficient conditions—and hence a characterization of the identified set—for two empirically relevant classes of specifications. An interesting feature of our approach is the use of the economic model under pairwise stability as a vehicle for effective dimension reduction. The paper then provides a quadratic programming algorithm that can be used to construct the identified sets. This algorithm is illustrated with a pair of simulation exercises.
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    Identifying Preferences in Networks With Bounded Degree
    (The Econometric Society, 2018) Paula, Áureo; Richards-Shubik, Seth; Tamer, Elie
    This paper provides a framework for identifying preferences in a large network where links are pairwise stable. Network formation models present difficulties for identification, especially when links can be interdependent, for example, when indirect connections matter. We show how one can use the observed proportions of various local network structures to learn about the underlying preference parameters. The key assumption for our approach restricts individuals to have bounded degree in equilibrium, implying a finite number of payoff‐relevant local structures. Our main result provides necessary conditions for parameters to belong to the identified set. We then develop a quadratic programming algorithm that can be used to construct this set. With further restrictions on preferences, we show that our conditions are also sufficient for pairwise stability and therefore characterize the identified set precisely. Overall, the use of both the economic model along with pairwise stability allows us to obtain effective dimension reduction.
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    Monte Carlo Confidence Sets for Identified Sets
    (Wiley, 2018-12-12) Chen, Xiaohong; Christensen, Timothy; Tamer, Elie
    It is generally difficult to know whether the parameters in nonlinear econometric models are point‐identified. We provide computationally attractive procedures to construct confidence sets (CSs) for identified sets of the full parameter vector and of subvectors in models defined through a likelihood or a vector of moment equalities or inequalities. The CSs are based on level sets of “optimal” criterion functions (such as likelihoods, optimally‐weighted or continuously‐updated GMM criterions). The level sets are constructed using cutoffs that are computed via Monte Carlo (MC) simulations from the quasi‐posterior distribution of the criterion. We establish new Bernstein–von Mises (or Bayesian Wilks) type theorems for the quasi‐posterior distributions of the quasi‐likelihood ratio (QLR) and profile QLR in partially‐identified models. These results imply that our MC CSs have exact asymptotic frequentist coverage for identified sets of full parameters and of subvectors in partially‐identified regular models, and have valid but potentially conservative coverage in models whose local tangent spaces are convex cones. Further, our MC CSs for identified sets of subvectors are shown to have exact asymptotic coverage in models with singularities. We provide local power properties and uniform validity of our CSs over classes of DGPs that include point‐ and partially‐identified models. Finally, we present two simulation experiments and two empirical examples: an airline entry game and a model of trade flows.