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}
}