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| Publications of Federico Bugni :chronological alphabetical by type listing:%% @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}, url = {http://dx.doi.org/10.1016/j.jeconom.2020.02.004}, 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}, url = {http://dx.doi.org/10.3982/QE1150}, 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}, url = {http://dx.doi.org/10.1080/01621459.2017.1375934}, 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}, url = {http://dx.doi.org/10.3982/QE490}, 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}, url = {http://dx.doi.org/10.1017/S0266466615000250}, 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}, url = {http://dx.doi.org/10.1016/j.jeconom.2014.10.013}, 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}, url = {http://dx.doi.org/10.1017/S0266466614000826}, 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}, url = {http://dx.doi.org/10.1108/S0731-9053(2013)0000032002}, 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}, url = {http://dx.doi.org/10.1007/s11698-011-0073-4}, 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}, url = {http://dx.doi.org/10.1017/S0266466612000023}, 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}, url = {http://dx.doi.org/10.3982/ECTA9604}, 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}, url = {http://dx.doi.org/10.3982/ECTA8056}, 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}, url = {http://dx.doi.org/10.1111/j.1368-423X.2008.00266.x}, 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} } | |
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