Confidence Intervals for Partially Identified Parameters

By Guido W. Imbens, Charles F. Manski. 2003

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View Paper: https://www.semanticscholar.org/paper/b66dd6a32e93c70b23794dea97248d7ca4c36a40

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In Statistics and Probability

Citations in the corpus

Identification and shape restrictions in nonparametric instrumental variables estimation (2012, methodology): “The asymptotic distribution can be used to carry out inference about the identified interval that contains L(g) and, using methods like those of Imbens and Manski (2004) and Stoye (2009), inference about the parameter L(g).”
Ignorance and uncertainty regions as inferential tools in a sensitivity analysis (2006, methodology): “Related ideas have been formulated and/or used by, e.g., Balke and Pearl (1997), Cochran (1977), Horowitz and Manski (2000), Imbens and Manski (2004), Joffe (2001), Nordheim (1984), Robins (1989) and Scharfstein, Manski and Anthony (2004).”
Inference for the identified set in partially identified econometric models (2006, positive): “In the context of confidence regions for identifiable parameters, this issue has been considered earlier by Imbens and Manski (2004), but to the best of our knowledge, we are the first in the literature to consider uniform consistency in level for confidence regions for the identified set.”
Nonparametric Bounds on the Causal Effect of University Studies on Job Opportunities Using Principal Stratification (2008, background): “The derivation of confidence limits is not trivial, and it can be approached in various ways, as explained by Imbens and Manski (2004).”
Partial Identification in Monotone Binary Models: Discrete Regressors and Interval Data (2008, methodology): “An alternative route is proposed by Imbens and Manski (2004) who changed focus by considering inference about the true value of the parameter (within an identi ed interval) and not about the interval in itself.”
Asymptotically exact inference in conditional moment inequality models (2011, background): “For example, in the interval regression model, the asymptotic distribution of this test statistic for parameters on the boundary of the identified set and the proper scaling needed to achieve it have so far been unknown in the generic case (see Section 2 for the definition of this model).”
Inference in Nonparametric Instrumental Variables With Partial Identification (2012, background): “Therefore, IN(R) diverges to infinity when Θ0 ∩ R = ∅, because in this case the infimum in (24) is taken over functions that”
Asymptotically Efficient Estimation of Models Defined by Convex Moment Inequalities (2014, methodology): “A third possible type of confidence region for Θ0(P0) covers each parameter in the identified set with pre-specified probability, as originally proposed in Imbens and Manski (2004). Formally, for a To the best of our knowledge, the two sided confidence region for the identified set hasn’t been devel”
Bayesian inference in a class of partially identified models (2016, background): “The robust Bayes results of Kitagawa (2012) establish the “bounds” on the posterior for a partially identified parameter due to considering a class of priors, and shows a sense in which this robust Bayes approach reconciles Bayesian and frequentist inference for a partially identified parameter, in ”
TESTING FOR A GENERAL CLASS OF FUNCTIONAL INEQUALITIES (2013, background): “There are different notions of efficiency for nonparametric tests and hence there is no compelling sense of an asymptotically optimal test for the hypothesis considered in this paper. See Nitikin (1995) and Bickel, Ritov, and Stoker (2006) for a general discussion.”
Inference for Parameters Defined by Moment Inequalities Using Generalized Moment Selection (2007, background): “…AG4, Romano and Shaikh (2005a,b), Rosen (2005), Beresteanu and Molinari (2006), Galichon and Henry (2006), Moon and Schorfheide (2006), Otsu (2006), Woutersen (2006), Bugni (2007a,b), Canay (2007), Guggenberger, Hahn, and Kim (2007), and Stoye (2007).1 The remainder of the paper is organized as…”