2017
DOI: 10.3982/qe490
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Inference for subvectors and other functions of partially identified parameters in moment inequality models

Abstract: This paper introduces a bootstrap-based inference method for functions of the parameter vector in a moment (in)equality model. These functions are restricted to be linear for two-sided testing problems, but may be nonlinear for one-sided testing problems. In the most common case, this function selects a subvector of the parameter, such as a single component. The new inference method we propose controls asymptotic size uniformly over a large class of data distributions and improves upon the two existing methods… Show more

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Cited by 90 publications
(131 citation statements)
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“…where c MR n is the critical value proposed in Bugni, Canay, and Shi (2017) and T n is any test statistic that they allowed for. The E-A-M algorithm can be used to compute the endpoints of this set so that the researcher may report an interval.…”
Section: Appendix B: Applying the E-a-m Algorithm To Profilingmentioning
confidence: 99%
See 1 more Smart Citation
“…where c MR n is the critical value proposed in Bugni, Canay, and Shi (2017) and T n is any test statistic that they allowed for. The E-A-M algorithm can be used to compute the endpoints of this set so that the researcher may report an interval.…”
Section: Appendix B: Applying the E-a-m Algorithm To Profilingmentioning
confidence: 99%
“…This brings us to our second contribution, namely, a general method to accurately and rapidly compute confidence intervals whose construction resembles (). Additional applications within partial identification include projection of confidence regions defined in Chernozhukov, Hong, and Tamer (), Andrews and Soares (), or Andrews and Shi (), as well as (with minor tweaking; see Appendix B) the confidence interval proposed in Bugni, Canay, and Shi (, BCS henceforth) and further discussed later. In an application to a point identified setting, Freyberger and Reeves (, Supplement Section S.3) used our method to construct uniform confidence bands for an unknown function of interest under (nonparametric) shape restrictions.…”
Section: Introductionmentioning
confidence: 99%
“… Inference procedures for subvectors have been further developed by Chaudhuri and Zivot (), Kaido, Molinari, and Stoye (), Andrews (), and Bugni, Canay, and Shi (). …”
mentioning
confidence: 99%
“…Finally, there is a large literature on techniques which seek to reduce sensitivity to the inclusion of slack moments in settings without nuisance parameters, including D. Andrews & Soares (2010), D. Andrews & Barwick (2012), Romano et al (2014a), and Cox & Shi (2019). , Bugni et al (2017), Belloni et al (2018), and Kaido et al (2019a) build on related ideas to reduce sensitivity to slack moments in models with nuisance parameters. If applied in our setting, however, these techniques would eliminate the linear structure which simplifies computation.…”
Section: Introductionmentioning
confidence: 99%