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| ## Publications of Adam M Rosen :recent first alphabetical combined listing:%% Journal Articles @article{fds325268, Author = {Molinari, F and Rosen, AM}, Title = {Comment}, Journal = {Journal of Business & Economic Statistics}, Volume = {26}, Number = {3}, Pages = {297-302}, Publisher = {Informa UK Limited}, Year = {2008}, Month = {July}, url = {http://dx.doi.org/10.1198/073500108000000088}, Abstract = {This article discusses how the analysis of Aradillas-Lopez and Tamer (2008) on the identification power of equilibrium in games can be extended to supermodular games. These games embody models that exhibit strategic complementarity, an important and empirically relevant class of economic models. In these games, the extreme points of the Nash equilibrium and rationalizable strategy sets coincide. We discuss how this result facilitates a comparative analysis of the relative identification power of equilibrium and weaker notions of rational behavior. As an illustrative example, we consider a differentiated product oligopoly pricing game in which firms' prices are strategic complements. © 2008 American Statistical Association.}, Doi = {10.1198/073500108000000088}, Key = {fds325268} } @article{fds325267, Author = {Rosen, AM}, Title = {Confidence sets for partially identified parameters that satisfy a finite number of moment inequalities}, Journal = {Journal of Econometrics}, Volume = {146}, Number = {1}, Pages = {107-117}, Publisher = {Elsevier BV}, Year = {2008}, Month = {September}, url = {http://dx.doi.org/10.1016/j.jeconom.2008.08.001}, Abstract = {This paper proposes a computationally simple way to construct confidence sets for a parameter of interest in models comprised of moment inequalities. Building on results from the literature on multivariate one-sided tests, I show how to test the hypothesis that any particular parameter value is logically consistent with the maintained moment inequalities. The associated test statistic has an asymptotic chi-bar-square distribution, and can be inverted to construct an asymptotic confidence set for the parameter of interest, even if that parameter is only partially identified. Critical values for the test are easily computed, and a Monte Carlo study demonstrates implementation and finite sample performance. © 2008 Elsevier B.V. All rights reserved.}, Doi = {10.1016/j.jeconom.2008.08.001}, Key = {fds325267} } @article{fds325266, Author = {Rosen, AM}, Title = {Set identification via quantile restrictions in short panels}, Journal = {Journal of Econometrics}, Volume = {166}, Number = {1}, Pages = {127-137}, Publisher = {Elsevier BV}, Year = {2012}, Month = {January}, url = {http://dx.doi.org/10.1016/j.jeconom.2011.06.011}, Abstract = {This paper studies the identifying power of conditional quantile restrictions in short panels with fixed effects. In contrast to classical fixed effects models with conditional mean restrictions, conditional quantile restrictions are not preserved by taking differences in the regression equation over time. This paper shows however that a conditional quantile restriction, in conjunction with a weak conditional independence restriction, provides bounds on quantiles of differences in time-varying unobservables across periods. These bounds carry observable implications for model parameters which generally result in set identification. The analysis of these bounds includes conditions for point identification of the parameter vector, as well as weaker conditions that result in point identification of individual parameter components. © 2011 Elsevier B.V. All rights reserved.}, Doi = {10.1016/j.jeconom.2011.06.011}, Key = {fds325266} } @article{fds325265, Author = {Nevo, A and Rosen, AM}, Title = {Identification with imperfect instruments}, Journal = {The Review of Economics and Statistics}, Volume = {94}, Number = {3}, Pages = {659-671}, Publisher = {MIT Press - Journals}, Year = {2012}, Month = {December}, url = {http://dx.doi.org/10.1162/REST_a_00171}, Abstract = {Dealing with endogenous regressors is a central challenge of applied research. The standard solution is to use instrumental variables that are assumed to be uncorrelated with unobservables. We instead allow the instrumental variable to be correlated with the error term, but we assume the correlation between the instrumental variable and the error term has the same sign as the correlation between the endogenous regressor and the error term and that the instrumental variable is less correlated with the error term than is the endogenous regressor. Using these assumptions, we derive analytic bounds for the parameters. We demonstrate that the method can generate useful (set) estimates by using it to estimate demand for differentiated products. © 2012 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.}, Doi = {10.1162/REST_a_00171}, Key = {fds325265} } @article{fds325264, Author = {Chernozhukov, V and Lee, S and Rosen, AM}, Title = {Intersection Bounds: Estimation and Inference}, Journal = {Econometrica}, Volume = {81}, Number = {2}, Pages = {667-737}, Publisher = {The Econometric Society}, Year = {2013}, Month = {March}, url = {http://dx.doi.org/10.3982/ECTA8718}, Abstract = {We develop a practical and novel method for inference on intersection bounds, namely bounds defined by either the infimum or supremum of a parametric or nonparametric function, or, equivalently, the value of a linear programming problem with a potentially infinite constraint set. We show that many bounds characterizations in econometrics, for instance bounds on parameters under conditional moment inequalities, can be formulated as intersection bounds. Our approach is especially convenient for models comprised of a continuum of inequalities that are separable in parameters, and also applies to models with inequalities that are nonseparable in parameters. Since analog estimators for intersection bounds can be severely biased in finite samples, routinely underestimating the size of the identified set, we also offer a median-bias-corrected estimator of such bounds as a by-product of our inferential procedures. We develop theory for large sample inference based on the strong approximation of a sequence of series or kernel-based empirical processes by a sequence of "penultimate" Gaussian processes. These penultimate processes are generally not weakly convergent, and thus are non-Donsker. Our theoretical results establish that we can nonetheless perform asymptotically valid inference based on these processes. Our construction also provides new adaptive inequality/moment selection methods. We provide conditions for the use of nonparametric kernel and series estimators, including a novel result that establishes strong approximation for any general series estimator admitting linearization, which may be of independent interest. © 2013 The Econometric Society.}, Doi = {10.3982/ECTA8718}, Key = {fds325264} } @article{fds325263, Author = {Chesher, A and Rosen, AM}, Title = {What do instrumental variable models deliver with discrete dependent variables?}, Journal = {American Economic Review}, Volume = {103}, Number = {3}, Pages = {557-562}, Publisher = {American Economic Association}, Year = {2013}, Month = {May}, url = {http://dx.doi.org/10.1257/aer.103.3.557}, Doi = {10.1257/aer.103.3.557}, Key = {fds325263} } @article{fds325262, Author = {Chesher, A and Rosen, AM and Smolinski, K}, Title = {An instrumental variable model of multiple discrete choice}, Journal = {Quantitative Economics}, Volume = {4}, Number = {2}, Pages = {157-196}, Publisher = {The Econometric Society}, Year = {2013}, Month = {July}, url = {http://dx.doi.org/10.3982/QE240}, Abstract = {This paper studies identification in multiple discrete choice models in which there may be endogenous explanatory variables, that is, explanatory variables that are not restricted to be distributed independently of the unobserved determinants of latent utilities. The model does not employ large support, special regressor, or control function restrictions; indeed, it is silent about the process that delivers values of endogenous explanatory variables, and in this respect it is incomplete. Instead, the model employs instrumental variable restrictions that require the existence of instrumental variables that are excluded from latent utilities and distributed independently of the unobserved components of utilities. We show that the model delivers set identification of latent utility functions and the distribution of unobserved heterogeneity, and we characterize sharp bounds on these objects. We develop easy-to-compute outer regions that, in parametric models, require little more calculation than what is involved in a conventional maximum likelihood analysis. The results are illustrated using a model that is essentially the conditional logit model of 41, but with potentially endogenous explanatory variables and instrumental variable restrictions. The method employed has wide applicability and for the first time brings instrumental variable methods to bear on structural models in which there are multiple unobservables in a structural equation. © 2013 Andrew Chesher, Adam M. Rosen, and Konrad Smolinski.}, Doi = {10.3982/QE240}, Key = {fds325262} } @article{fds351132, Author = {Chernozhukov, V and Kim, W and Lee, SS and Rosen, A}, Title = {Implementing intersection bounds in Stata}, Year = {2013}, Month = {August}, Abstract = {We present the clrbound, clr2bound, clr3bound and clrtest commands for estimation and inference developed by Chernozhukov et al. (2013). The commands clrbound, clr2bound and clr3bound provide bound estimates that can be used directly for estimation or to construct asymptotically valid confidence sets. The command clrbound provides bound estimates for one-sided lower or upper intersection bounds on a parameter, while clr2bound and clr3bound provide two-sided bound estimates based on both lower and upper intersection bounds. clr2bound uses Bonferroni's inequality to construct two-sided bounds, whereas clr3bound inverts a hypothesis test. The former can be used to perform asymptotically valid inference on the identified set or the parameter, while the latter can be used to provide asymptotically valid and generally tighter confidence intervals for the parameter. clrtest performs an intersection bound test of the hypothesis that a collection of lower intersection bounds is no greater than zero. Inversion of this test can be used to construct confidence sets based on conditional moment inequalities as described in Chernozhukov et al. (2013). The commands include parametric, series and local linear estimation procedures and can be installed from within Stata by typing 'ssc install clrbound'.}, Key = {fds351132} } @article{fds351131, Author = {Chernozhukov, V and Kim, W and Lee, SS and Rosen, A}, Title = {Implementing intersection bounds in Stata}, Year = {2014}, Month = {May}, Abstract = {We present the clrbound, clr2bound, clr3bound, and clrtest commands for estimation and inference on intersection bounds as developed by Chernozhukov et al. (2013). The intersection bounds framework encompasses situations where a population parameter of interest is partially identi?ed by a collection of consistently estimable upper and lower bounds. The identi?ed set for the parameter is the intersection of regions de?ned by this collection of bounds. More generally, the methodology can be applied to settings where an estimable function of a vector-valued parameter is bounded from above and below, as is the case when the identi?ed set is characterized by conditional moment inequalities. The commands clrbound, clr2bound, and clr3bound provide bound estimates that can be used directly for estimation or to construct asymptotically valid con?dence sets. clrtest performs an intersection bound test of the hypothesis that a collection of lower intersection bounds is no greater than zero. The command clrbound provides bound estimates for one-sided lower or upper intersection bounds on a parameter, while clr2bound and clr3bound provide two-sided bound estimates based on both lower and upper intersection bounds. clr2bound uses Bonferroni’s inequality to construct two-sided bounds that can be used to perform asymptotically valid inference on the identi?ed set or the parameter of interest, whereas clr3bound provides a generally tighter con?dence interval for the parameter by inverting the hypothesis test performed by clrtest. More broadly, inversion of this test can also be used to construct con?dence sets based on conditional moment inequalities as described in Chernozhukov et al. (2013). The commands include parametric, series, and local linear estimation procedures, and can be installed from within STATA by typing “ssc install clrbound”.}, Key = {fds351131} } @article{fds325261, Author = {Chesher, A and Rosen, AM}, Title = {An instrumental variable random-coefficients model for binary outcomes.}, Journal = {The Econometrics Journal}, Volume = {17}, Number = {2}, Pages = {S1-S19}, Publisher = {Oxford University Press (OUP)}, Year = {2014}, Month = {June}, url = {http://dx.doi.org/10.1111/ectj.12018}, Abstract = {In this paper, we study a random-coefficients model for a binary outcome. We allow for the possibility that some or even all of the explanatory variables are arbitrarily correlated with the random coefficients, thus permitting endogeneity. We assume the existence of observed instrumental variables <i>Z</i> that are jointly independent with the random coefficients, although we place no structure on the joint determination of the endogenous variable <i>X</i> and instruments <i>Z</i>, as would be required for a control function approach. The model fits within the spectrum of generalized instrumental variable models, and we thus apply identification results from our previous studies of such models to the present context, demonstrating their use. Specifically, we characterize the identified set for the distribution of random coefficients in the binary response model with endogeneity via a collection of conditional moment inequalities, and we investigate the structure of these sets by way of numerical illustration.}, Doi = {10.1111/ectj.12018}, Key = {fds325261} } @article{fds325683, Author = {Chernozhukov, V and Kim, W and Lee, S and Rosen, AM}, Title = {Implementing intersection bounds in stata}, Journal = {Stata Journal}, Volume = {15}, Number = {1}, Pages = {21-44}, Year = {2015}, Month = {April}, url = {http://dx.doi.org/10.1177/1536867x1501500103}, Abstract = {We present the clrbound, clr2bound, clr3bound, and clrtest commands for estimation and inference on intersection bounds as developed by Chernozhukov, Lee, and Rosen (2013, Econometrica 81: 667–737). The intersection bounds framework encompasses situations where a population parameter of interest is partially identified by a collection of consistently estimable upper and lower bounds. The identified set for the parameter is the intersection of regions defined by this collection of bounds. More generally, the methodology can be applied to settings where an estimable function of a vector-valued parameter is bounded from above and below, as is the case when the identified set is characterized by conditional moment inequalities. The commands clrbound, clr2bound, and clr3bound provide bound estimates that can be used directly for estimation or to construct asymptotically valid confidence sets. clrtest performs an intersection bound test of the hypothesis that a collection of lower intersection bounds is no greater than zero. The command clrbound provides bound estimates for one-sided lower or upper intersection bounds on a parameter, while clr2bound and clr3bound provide two-sided bound estimates using both lower and upper intersection bounds. clr2bound uses Bonferroni’s inequality to construct two-sided bounds that can be used to perform asymptotically valid inference on the identified set or the parameter of interest, whereas clr3bound provides a generally tighter confidence interval for the parameter by inverting the hypothesis test performed by clrtest. More broadly, inversion of this test can also be used to construct confidence sets based on conditional moment inequalities as described in Chernozhukov, Lee, and Rosen (2013). The commands include parametric, series, and local linear estimation procedures.}, Doi = {10.1177/1536867x1501500103}, Key = {fds325683} } @article{fds326189, Author = {Ho, K and Rosen, AM}, Title = {Partial Identification in Applied Research: Benefits and Challenges}, Year = {2015}, Month = {October}, Key = {fds326189} } @article{fds326824, Author = {Chesher, A and Rosen, AM}, Title = {Generalized Instrumental Variable Models}, Journal = {Econometrica}, Volume = {85}, Number = {3}, Pages = {959-989}, Publisher = {The Econometric Society}, Year = {2017}, Month = {May}, url = {http://dx.doi.org/10.3982/ECTA12223}, Abstract = {This paper develops characterizations of identified sets of structures and structural features for complete and incomplete models involving continuous or discrete variables. Multiple values of unobserved variables can be associated with particular combinations of observed variables. This can arise when there are multiple sources of heterogeneity, censored or discrete endogenous variables, or inequality restrictions on functions of observed and unobserved variables. The models generalize the class of incomplete instrumental variable (IV) models in which unobserved variables are single-valued functions of observed variables. Thus the models are referred to as generalized IV (GIV) models, but there are important cases in which instrumental variable restrictions play no significant role. Building on a definition of observational equivalence for incomplete models the development uses results from random set theory that guarantee that the characterizations deliver sharp bounds, thereby dispensing with the need for case-by-case proofs of sharpness. The use of random sets defined on the space of unobserved variables allows identification analysis under mean and quantile independence restrictions on the distributions of unobserved variables conditional on exogenous variables as well as under a full independence restriction. The results are used to develop sharp bounds on the distribution of valuations in an incomplete model of English auctions, improving on the pointwise bounds available until now. Application of many of the results of the paper requires no familiarity with random set theory.}, Doi = {10.3982/ECTA12223}, Key = {fds326824} } %% Chapters in Books @misc{fds358015, Author = {Chesher, A and Rosen, AM}, Title = {Counterfactual worlds}, Journal = {Annals of Economics and Statistics}, Volume = {142}, Pages = {311-335}, Year = {2021}, Month = {June}, url = {http://dx.doi.org/10.15609/ANNAECONSTAT2009.142.0311}, Abstract = {We study an extension of a treatment effect model in which an observed discrete classifier indicates which one of a set of counterfactual processes occurs, each of which may result in the realization of several endogenous outcomes. In addition to the classifier indicating which process was realized, other observed outcomes are delivered by the particular counterfactual process. Models of the counterfactual processes can be incomplete in the sense that even with knowledge of the values of observed exogenous and unobserved variables they may not deliver a unique value of the endogenous outcomes. Thus, relative to the usual treatment effect models, counterfactual outcomes are replaced by counterfactual processes. The determination of endogenous variables in these counterfactual processes may be modeled by the researcher, and impacted by observable exogenous variables restricted to be independent of certain unobservable variables as in instrumental variable models. We study the identifying power of models of this sort that incorporate (i) conditional independence restrictions under which unobserved variables and the classifier variable are stochastically independent conditional on some of the observed exogenous variables and (ii) marginal independence restrictions under which unobservable variables and a subset of the exogenous variables are independently distributed. Building on results in Chesher and Rosen (2017), we characterize the identifying power of these models for fundamental structural relationships and probability distributions of unobservable heterogeneity. JEL Codes: C10, C20, C26, C30, C36, C51.}, Doi = {10.15609/ANNAECONSTAT2009.142.0311}, Key = {fds358015} } | |

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