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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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