Confidence Intervals for Projections of Partially Identified Parameters

Hiroaki Kaido, Francesca Molinari and Jörg Stoye

Papers from arXiv.org

Abstract: We propose a bootstrap-based calibrated projection procedure to build confidence intervals for single components and for smooth functions of a partially identified parameter vector in moment (in)equality models. The method controls asymptotic coverage uniformly over a large class of data generating processes. The extreme points of the calibrated projection confidence interval are obtained by extremizing the value of the function of interest subject to a proper relaxation of studentized sample analogs of the moment (in)equality conditions. The degree of relaxation, or critical level, is calibrated so that the function of theta, not theta itself, is uniformly asymptotically covered with prespecified probability. This calibration is based on repeatedly checking feasibility of linear programming problems, rendering it computationally attractive. Nonetheless, the program defining an extreme point of the confidence interval is generally nonlinear and potentially intricate. We provide an algorithm, based on the response surface method for global optimization, that approximates the solution rapidly and accurately, and we establish its rate of convergence. The algorithm is of independent interest for optimization problems with simple objectives and complicated constraints. An empirical application estimating an entry game illustrates the usefulness of the method. Monte Carlo simulations confirm the accuracy of the solution algorithm, the good statistical as well as computational performance of calibrated projection (including in comparison to other methods), and the algorithm's potential to greatly accelerate computation of other confidence intervals.

Date: 2016-01, Revised 2019-06
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (49)

Published in Econometrics, Volume 87, Issue 4, July 2019, Pages 1397-1432

Downloads: (external link)
http://arxiv.org/pdf/1601.00934 Latest version (application/pdf)

Related works:
Journal Article: Confidence Intervals for Projections of Partially Identified Parameters (2019)
Working Paper: Confi dence Intervals for Projections of Partially Identifi ed Parameters (2019)
Working Paper: Confidence intervals for projections of partially identified parameters (2017)
Working Paper: Confidence intervals for projections of partially identified parameters (2017)
Working Paper: Confidence intervals for projections of partially identified parameters (2016)
Working Paper: Confi dence Intervals for Projections of Partially Identi fied Parameters (2016)
Working Paper: Confidence intervals for projections of partially identified parameters (2016)
Working Paper: Confidence Intervals for Projections of Partially Identified Parameters (2016)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1601.00934

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().