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| Publications of Adam M Rosen :recent first alphabetical by type listing:
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@article{fds325268,
Author = {Molinari, F and Rosen, AM},
Title = {Comment},
Journal = {Journal of Business and 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 = {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}
}
@article{fds366380,
Author = {Chesher, A and Rosen, AM},
Title = {Generalized instrumental variable models, methods, and
applications⋆},
Journal = {Handbook of Econometrics},
Volume = {7},
Pages = {1-110},
Year = {2020},
Month = {January},
url = {http://dx.doi.org/10.1016/bs.hoe.2019.11.001},
Abstract = {This chapter sets out the extension of the scope of the
classical IV model to cases in which unobserved variables
are set-valued functions of observed variables. The
resulting Generalized IV (GIV) models can be used when
outcomes are discrete while unobserved variables are
continuous, when there are rich specifications of
heterogeneity as in random coefficient models, and when
there are inequality restrictions constraining observed
outcomes and unobserved variables. There are many other
applications and classical IV models arise as a special
case. The chapter provides characterizations of the
identified sets delivered by GIV models. It gives details of
the application of GIV analysis to models with an interval
censored endogenous variable and to binary outcome models
– for example probit models – with endogenous
explanatory variables. It illustrates how the identified
sets delivered by GIV models can be represented by moment
inequality characterizations that have been the focus of
recently developed methods for inference. An empirical
application to a binary outcome model of female labor force
participation is worked through in detail.},
Doi = {10.1016/bs.hoe.2019.11.001},
Key = {fds366380}
}
@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}
}
@article{fds359883,
Author = {Aradillas-López, A and Rosen, AM},
Title = {Inference in ordered response games with complete
information},
Journal = {Journal of Econometrics},
Volume = {226},
Number = {2},
Pages = {451-476},
Year = {2022},
Month = {February},
url = {http://dx.doi.org/10.1016/j.jeconom.2021.09.017},
Abstract = {We study inference in complete information games with
discrete strategy spaces. Unlike binary games, we allow for
rich strategy spaces and we only assume that they are
ordinal in nature. We derive observable implications of
equilibrium play under mild shape restrictions on payoff
functions, and we characterize sharp identified sets for
model parameters. We propose a novel inference method based
on a test statistic that embeds conditional moment
inequalities implied by equilibrium behavior. Our statistic
has asymptotically pivotal properties that depend on the
measure of contact sets, to which our statistic adapts
automatically. In the case of two players and strategic
substitutes we show that certain payoff parameters are point
identified under mild conditions. We embed conventional
point estimates for these parameters in our conditional
moment inequality test statistic in order to perform
inference on the remaining (partially identified)
parameters. We apply our method to model the number of
stores operated by Lowe's and Home Depot in geographic
markets and perform inference on several quantities of
economic interest.},
Doi = {10.1016/j.jeconom.2021.09.017},
Key = {fds359883}
}
@article{fds364332,
Author = {Aristodemou, E and Rosen, AM},
Title = {A discrete choice model for partially ordered
alternatives},
Journal = {Quantitative Economics},
Volume = {13},
Number = {3},
Pages = {863-906},
Year = {2022},
Month = {July},
url = {http://dx.doi.org/10.3982/QE1497},
Abstract = {In this paper, we analyze a discrete choice model for
partially ordered alternatives. The alternatives are
differentiated along two dimensions: the first an unordered
“horizontal” dimension, and the second an ordered
“vertical” dimension. The model can be used in
circumstances in which individuals choose among products of
different brands, wherein each brand offers an ordered
choice menu, for example, by offering products of varying
quality. The unordered–ordered nature of the discrete
choice problem is used to characterize the identified set of
model parameters. Following an initial nonparametric
analysis that relies on shape restrictions inherent in the
ordered dimension of the problem, we then provide a
specialized analysis for parametric specifications that
generalize common ordered choice models. We characterize
conditional choice probabilities as a function of model
primitives with particular analysis focusing on cases in
which unobservable taste for quality of each brand offering
is multivariate normally distributed. We provide explicit
formulae used for estimation and inference via maximum
likelihood, and we consider inference based on Wald and
quasi-likelihood ratio statistics, the latter of which can
be robust to a possible lack of point identification. An
empirical illustration is conducted using data on razor
blade purchases in which each brand has product offerings
vertically differentiated by quality.},
Doi = {10.3982/QE1497},
Key = {fds364332}
}
@article{fds369742,
Author = {Chesher, A and Kim, D and Rosen, AM},
Title = {IV methods for Tobit models},
Journal = {Journal of Econometrics},
Volume = {235},
Number = {2},
Pages = {1700-1724},
Year = {2023},
Month = {August},
url = {http://dx.doi.org/10.1016/j.jeconom.2023.01.010},
Abstract = {This paper studies models of processes generating censored
outcomes with endogenous explanatory variables and
instrumental variable restrictions. Tobit-type left
censoring at zero is the primary focus in the exposition.
Extension to stochastic censoring is sketched. The models do
not specify the process determining endogenous explanatory
variables and they do not embody restrictions justifying
control function approaches. Consequently, they can be
partially or point identifying. Identified sets are
characterized and it is shown how inference can be performed
on scalar functions of partially identified parameters when
exogenous variables have rich support. In an application
using data on UK household tobacco expenditures inference is
conducted on the coefficient of an endogenous total
expenditure variable with and without a Gaussian
distributional restriction on the unobservable and compared
with the results obtained using a point identifying complete
triangular model.},
Doi = {10.1016/j.jeconom.2023.01.010},
Key = {fds369742}
}
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