Publications of Federico Bugni
%% Working Papers
@article{fds349168,
Author = {Bugni, FA and Canay, IA},
Title = {Testing continuity of a density via g-order statistics in
the regression discontinuity design},
Journal = {Journal of Econometrics},
Volume = {221},
Number = {1},
Pages = {138-159},
Year = {2021},
Month = {March},
Abstract = {In the regression discontinuity design (RDD), it is common
practice to assess the credibility of the design by testing
the continuity of the density of the running variable at the
cut-off, e.g., McCrary (2008). In this paper we propose an
approximate sign test for continuity of a density at a point
based on the so-called g-order statistics, and study its
properties under two complementary asymptotic frameworks. In
the first asymptotic framework, the number q of observations
local to the cut-off is fixed as the sample size n diverges
to infinity, while in the second framework q diverges to
infinity slowly as n diverges to infinity. Under both of
these frameworks, we show that the test we propose is
asymptotically valid in the sense that it has limiting
rejection probability under the null hypothesis not
exceeding the nominal level. More importantly, the test is
easy to implement, asymptotically valid under weaker
conditions than those used by competing methods, and
exhibits finite sample validity under stronger conditions
than those needed for its asymptotic validity. In a
simulation study, we find that the approximate sign test
provides good control of the rejection probability under the
null hypothesis while remaining competitive under the
alternative hypothesis. We finally apply our test to the
design in Lee (2008), a well-known application of the RDD to
study incumbency advantage.},
Doi = {10.1016/j.jeconom.2020.02.004},
Key = {fds349168}
}
@article{fds347333,
Author = {Bugni, FA and Canay, IA and Shaikh, AM},
Title = {Inference under covariate-adaptive randomization with
multiple treatments},
Journal = {Quantitative Economics},
Volume = {10},
Number = {4},
Pages = {1747-1785},
Year = {2019},
Month = {November},
Abstract = {This paper studies inference in randomized controlled trials
with covariate-adaptive randomization when there are
multiple treatments. More specifically, we study in this
setting inference about the average effect of one or more
treatments relative to other treatments or a control. As in
Bugni, Canay, and Shaikh (2018), covariate-adaptive
randomization refers to randomization schemes that first
stratify according to baseline covariates and then assign
treatment status so as to achieve “balance” within each
stratum. Importantly, in contrast to Bugni, Canay, and
Shaikh (2018), we not only allow for multiple treatments,
but further allow for the proportion of units being assigned
to each of the treatments to vary across strata. We first
study the properties of estimators derived from a “fully
saturated” linear regression, that is, a linear regression
of the outcome on all interactions between indicators for
each of the treatments and indicators for each of the
strata. We show that tests based on these estimators using
the usual heteroskedasticity-consistent estimator of the
asymptotic variance are invalid in the sense that they may
have limiting rejection probability under the null
hypothesis strictly greater than the nominal level; on the
other hand, tests based on these estimators and suitable
estimators of the asymptotic variance that we provide are
exact in the sense that they have limiting rejection
probability under the null hypothesis equal to the nominal
level. For the special case in which the target proportion
of units being assigned to each of the treatments does not
vary across strata, we additionally consider tests based on
estimators derived from a linear regression with “strata
fixed effects,” that is, a linear regression of the
outcome on indicators for each of the treatments and
indicators for each of the strata. We show that tests based
on these estimators using the usual heteroskedasticity-consistent
estimator of the asymptotic variance are conservative in the
sense that they have limiting rejection probability under
the null hypothesis no greater than and typically strictly
less than the nominal level, but tests based on these
estimators and suitable estimators of the asymptotic
variance that we provide are exact, thereby generalizing
results in Bugni, Canay, and Shaikh (2018) for the case of a
single treatment to multiple treatments. A simulation study
and an empirical application illustrate the practical
relevance of our theoretical results.},
Doi = {10.3982/QE1150},
Key = {fds347333}
}
@article{fds336353,
Author = {Bugni, FA and Canay, IA and Shaikh, AM},
Title = {Inference Under Covariate-Adaptive Randomization},
Journal = {Journal of the American Statistical Association},
Volume = {113},
Number = {524},
Pages = {1784-1796},
Publisher = {Informa UK Limited},
Year = {2018},
Month = {October},
Abstract = {This article studies inference for the average treatment
effect in randomized controlled trials with
covariate-adaptive randomization. Here, by
covariate-adaptive randomization, we mean randomization
schemes that first stratify according to baseline covariates
and then assign treatment status so as to achieve
“balance” within each stratum. Our main requirement is
that the randomization scheme assigns treatment status
within each stratum so that the fraction of units being
assigned to treatment within each stratum has a well behaved
distribution centered around a proportion π as the sample
size tends to infinity. Such schemes include, for example,
Efron’s biased-coin design and stratified block
randomization. When testing the null hypothesis that the
average treatment effect equals a prespecified value in such
settings, we first show the usual two-sample t-test is
conservative in the sense that it has limiting rejection
probability under the null hypothesis no greater than and
typically strictly less than the nominal level. We show,
however, that a simple adjustment to the usual standard
error of the two-sample t-test leads to a test that is exact
in the sense that its limiting rejection probability under
the null hypothesis equals the nominal level. Next, we
consider the usual t-test (on the coefficient on treatment
assignment) in a linear regression of outcomes on treatment
assignment and indicators for each of the strata. We show
that this test is exact for the important special case of
randomization schemes with π=1/2, but is otherwise
conservative. We again provide a simple adjustment to the
standard errors that yields an exact test more generally.
Finally, we study the behavior of a modified version of a
permutation test, which we refer to as the
covariate-adaptive permutation test, that only permutes
treatment status for units within the same stratum. When
applied to the usual two-sample t-statistic, we show that
this test is exact for randomization schemes with π=1/2 and
that additionally achieve what we refer to as “strong
balance.” For randomization schemes with π≠1/2, this
test may have limiting rejection probability under the null
hypothesis strictly greater than the nominal level. When
applied to a suitably adjusted version of the two-sample
t-statistic, however, we show that this test is exact for
all randomization schemes that achieve “strong balance,”
including those with π≠1/2. A simulation study confirms
the practical relevance of our theoretical results. We
conclude with recommendations for empirical practice and an
empirical illustration. Supplementary materials for this
article are available online.},
Doi = {10.1080/01621459.2017.1375934},
Key = {fds336353}
}
@article{fds325923,
Author = {Bugni, FA and Canay, IA and Shi, X},
Title = {Inference for subvectors and other functions of partially
identified parameters in moment inequality
models},
Journal = {Quantitative Economics},
Volume = {8},
Number = {1},
Pages = {1-38},
Publisher = {The Econometric Society},
Year = {2017},
Month = {March},
Doi = {10.3982/QE490},
Key = {fds325923}
}
@article{fds238049,
Author = {Aucejo, EM and Bugni, FA and Hotz, VJ},
Title = {Identification and inference on regressions with missing
covariate data},
Journal = {Econometric Theory},
Volume = {33},
Number = {1},
Pages = {196-241},
Publisher = {Cambridge University Press (CUP)},
Year = {2017},
Month = {February},
ISSN = {0266-4666},
Abstract = {This paper examines the problem of identification and
inference on a conditional moment condition model with
missing data, with special focus on the case when the
conditioning covariates are missing. We impose no assumption
on the distribution of the missing data and we confront the
missing data problem by using a worst case scenario
approach. We characterize the sharp identified set and argue
that this set is usually too complex to compute or to use
for inference. Given this difficulty, we consider the
construction of outer identified sets (i.e. supersets of the
identified set) that are easier to compute and can still
characterize the parameter of interest. Two different outer
identification strategies are proposed. Both of these
strategies are shown to have nontrivial identifying power
and are relatively easy to use and combine for inferential
purposes.},
Doi = {10.1017/S0266466615000250},
Key = {fds238049}
}
@article{fds238050,
Author = {Bugni, FA and Canay, IA and Shi, X},
Title = {Specification tests for partially identified models defined
by moment inequalities},
Journal = {Journal of Econometrics},
Volume = {185},
Number = {1},
Pages = {259-282},
Publisher = {Elsevier BV},
Year = {2015},
Month = {January},
ISSN = {0304-4076},
Abstract = {This paper studies the problem of specification testing in
partially identified models defined by moment
(in)equalities. This problem has not been directly addressed
in the literature, although several papers have suggested a
test based on checking whether confidence sets for the
parameters of interest are empty or not, referred to as Test
BP. We propose two new specification tests, denoted Test RS
and Test RC, that achieve uniform asymptotic size control
and dominate Test BP in terms of power in any finite sample
and in the asymptotic limit.},
Doi = {10.1016/j.jeconom.2014.10.013},
Key = {fds238050}
}
@article{fds323212,
Author = {Bugni, FA},
Title = {COMPARISON of INFERENTIAL METHODS in PARTIALLY IDENTIFIED
MODELS in TERMS of ERROR in COVERAGE PROBABILITY},
Journal = {Econometric Theory},
Volume = {32},
Number = {1},
Pages = {187-242},
Year = {2014},
Month = {October},
Abstract = {This paper considers the problem of coverage of the elements
of the identified set in a class of partially identified
econometric models with a prespecified probability. In order
to conduct inference in partially identified econometric
models defined by moment (in)equalities, the literature has
proposed three methods: bootstrap, subsampling, and
asymptotic approximation. The objective of this paper is to
compare these methods in terms of the rate at which they
achieve the desired coverage level, i.e., in terms of the
rate at which the error in the coverage probability (ECP)
converges to zero. Under certain conditions, we show that
the ECP of the bootstrap and the ECP of the asymptotic
approximation converge to zero at the same rate, which is a
faster rate than that of the ECP of subsampling methods. As
a consequence, under these conditions, the bootstrap and the
asymptotic approximation produce inference that is more
precise than subsampling. A Monte Carlo simulation study
confirms that these results are relevant in finite
samples.},
Doi = {10.1017/S0266466614000826},
Key = {fds323212}
}
@article{fds238052,
Author = {Arcidiacono, P and Bayer, P and Bugni, FA and James,
J},
Title = {Approximating High-Dimensional Dynamic Models: Sieve Value
Function Iteration},
Journal = {Advances in Econometrics},
Volume = {31},
Pages = {45-95},
Publisher = {Emerald Group Publishing Limited},
Year = {2013},
Month = {January},
ISSN = {0731-9053},
Abstract = {Many dynamic problems in economics are characterized by
large state spaces which make both computing and estimating
the model infeasible. We introduce a method for
approximating the value function of highdimensional dynamic
models based on sieves and establish results for the (a)
consistency, (b) rates of convergence, and (c) bounds on the
error of approximation. We embed this method for
approximating the solution to the dynamic problem within an
estimation routine and prove that it provides consistent
estimates of the modelik's parameters. We provide Monte
Carlo evidence that our method can successfully be used to
approximate models that would otherwise be infeasible to
compute, suggesting that these techniques may substantially
broaden the class of models that can be solved and
estimated. Copyright © 2013 by Emerald Group Publishing
Limited.},
Doi = {10.1108/S0731-9053(2013)0000032002},
Key = {fds238052}
}
@article{fds238055,
Author = {Bugni, FA},
Title = {Child labor legislation: Effective, benign, both, or
neither?},
Journal = {Cliometrica},
Volume = {6},
Number = {3},
Pages = {223-248},
Publisher = {Springer Nature},
Year = {2012},
Month = {October},
ISSN = {1863-2505},
Abstract = {This paper explores the relationship between the
state-specific child labor legislation and the decline in
child labor that occurred in the US between 1880 and 1900.
The existing literature that addresses this question uses a
difference-in-difference estimation technique. We contribute
to this literature in two ways. First, we argue that this
estimation technique can produce misleading results due to
(a) the possibility of multiplicity of equilibria and (b)
the non-linearity of the underlying econometric model.
Second, we develop an empirical strategy to identify the
mechanism by which the legislation affected child labor
decisions. In particular, besides establishing whether the
legislation was effective or not, our analysis may determine
whether the legislation constituted a benign policy or not,
i. e., whether the legislation constrained the behavior of
families (not benign) or whether it changed the labor market
to a new equilibrium in which families voluntarily respected
the law (benign). © 2011 Springer-Verlag.},
Doi = {10.1007/s11698-011-0073-4},
Key = {fds238055}
}
@article{fds238056,
Author = {Bugni, FA},
Title = {Specification test for missing functional
data},
Journal = {Econometric Theory},
Volume = {28},
Number = {5},
Pages = {959-1002},
Publisher = {Cambridge University Press (CUP)},
Year = {2012},
Month = {October},
ISSN = {0266-4666},
Abstract = {Economic data are frequently generated by stochastic
processes that can be modeled as realizations of random
functions (functional data). This paper adapts the
specification test for functional data developed by Bugni,
Hall, Horowitz, and Neumann (2009, Econometrics Journal12,
S1a-S18) to the presence of missing observations. By using a
worst case scenario approach, our method is able to extract
the information available in the observed portion of the
data while being agnostic about the nature of the missing
observations. The presence of missing data implies that our
test will not only result in the rejection or lack of
rejection of the null hypothesis, but it may also be
inconclusive. Under the null hypothesis, our specification
test will reject the null hypothesis with a probability
that, in the limit, does not exceed the significance level
of the test. Moreover, the power of the test converges to
one whenever the distribution of the observations conveys
that the null hypothesis is false. Monte Carlo evidence
shows that the test may produce informative results (either
rejection or lack of rejection of the null hypothesis) even
under the presence of significant amounts of missing data.
The procedure is illustrated by testing whether the
Burdetta-Mortensen labor market model is the correct
framework for wage paths constructed from the National
Longitudinal Survery of Youth, 1979 survey. © 2012
Cambridge University Press.},
Doi = {10.1017/S0266466612000023},
Key = {fds238056}
}
@article{fds238054,
Author = {Bugni, FA and Canay, IA and Guggenberger, P},
Title = {Distortions of Asymptotic Confidence Size in Locally
Misspecified Moment Inequality Models},
Journal = {Econometrica},
Volume = {80},
Number = {4},
Pages = {1741-1768},
Publisher = {The Econometric Society},
Year = {2012},
Month = {July},
ISSN = {0012-9682},
Abstract = {This paper studies the behavior, under local
misspecification, of several confidence sets (CSs) commonly
used in the literature on inference in moment (in)equality
models. We propose the amount of asymptotic confidence size
distortion as a criterion to choose among competing
inference methods. This criterion is then applied to compare
across test statistics and critical values employed in the
construction of CSs. We find two important results under
weak assumptions. First, we show that CSs based on
subsampling and generalized moment selection (Andrews and
Soares (2010)) suffer from the same degree of asymptotic
confidence size distortion, despite the fact that
asymptotically the latter can lead to CSs with strictly
smaller expected volume under correct model specification.
Second, we show that the asymptotic confidence size of CSs
based on the quasi-likelihood ratio test statistic can be an
arbitrary small fraction of the asymptotic confidence size
of CSs based on the modified method of moments test
statistic. © 2012 The Econometric Society.},
Doi = {10.3982/ECTA9604},
Key = {fds238054}
}
@article{fds238057,
Author = {Bugni, FA},
Title = {Bootstrap inference in partially identified models defined
by moment inequalities: Coverage of the identified
set},
Journal = {Econometrica},
Volume = {78},
Number = {2},
Pages = {735-753},
Publisher = {The Econometric Society},
Year = {2010},
Month = {March},
ISSN = {0012-9682},
Abstract = {This paper introduces a novel bootstrap procedure to perform
inference in a wide class of partially identified
econometric models. We consider econometric models defined
by finitely many weak moment inequalities,2 which encompass
many applications of economic interest. The objective of our
inferential procedure is to cover the identified set with a
prespecified probability.3 We compare our bootstrap
procedure, a competing asymptotic approximation, and
subsampling procedures in terms of the rate at which they
achieve the desired coverage level, also known as the error
in the coverage probability. Under certain conditions, we
show that our bootstrap procedure and the asymptotic
approximation have the same order of error in the coverage
probability, which is smaller than that obtained by using
subsampling. This implies that inference based on our
bootstrap and asymptotic approximation should eventually be
more precise than inference based on subsampling. A Monte
Carlo study confirms this finding in a small sample
simulation. © 2010 The Econometric Society.},
Doi = {10.3982/ECTA8056},
Key = {fds238057}
}
@article{fds238053,
Author = {Bugni, FA and Hall, P and Horowitz, JL and Neumann,
GR},
Title = {Goodness-of-fit tests for functional data},
Journal = {The Econometrics Journal},
Volume = {12},
Number = {SUPPL. 1},
Pages = {S1-S18},
Year = {2009},
Month = {July},
ISSN = {1368-4221},
Abstract = {Economic data are frequently generated by stochastic
processes that can be modelled as occurring in continuous
time. That is, the data are treated as realizations of a
random function (functional data). Sometimes an economic
theory model specifies the process up to a
finite-dimensional parameter. This paper develops a test of
the null hypothesis that a given functional data set was
generated by a specified parametric model of a
continuous-time process. The alternative hypothesis is
non-parametric. A random function is a form of
infinite-dimensional random variable, and the test presented
here a generalization of the familiar Cramér-von Mises test
to an infinite dimensional random variable. The test is
illustrated by using it to test the hypothesis that a sample
of wage paths was generated by a certain equilibrium job
search model. Simulation studies show that the test has good
finite-sample performance. © Journal compilation © 2009
Royal Economic Society.},
Doi = {10.1111/j.1368-423X.2008.00266.x},
Key = {fds238053}
}