Faculty DatabaseEconomics Arts & Sciences Duke University |
||

HOME > Arts & Sciences > Economics > Faculty | Search Help Login |

| ## Publications of Federico Bugni :chronological alphabetical combined listing:%% Working Papers @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 = {© 2018, © 2018 American Statistical Association. 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 = {© Cambridge University Press 2015. 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 = {© 2014 Elsevier B.V. All rights reserved. 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 = {Copyright © Cambridge University Press 2014. 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} } | |

Duke University * Arts & Sciences * Economics * Faculty * Research * Staff * Master's * Ph.D. * Reload * Login |