COMPARISON of INFERENTIAL METHODS in PARTIALLY IDENTIFIED MODELS in TERMS of ERROR in COVERAGE PROBABILITY

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Bugni, FA

Published Date

  • October 10, 2014

Published In

Volume / Issue

  • 32 / 1

Start / End Page

  • 187 - 242

Electronic International Standard Serial Number (EISSN)

  • 1469-4360

International Standard Serial Number (ISSN)

  • 0266-4666

Digital Object Identifier (DOI)

  • 10.1017/S0266466614000826

Citation Source

  • Scopus