Bias-corrected confidence intervals in a class of linear inverse problems

Jean-Pierre Florens, Joel L. Horowitz and Ingred van Keilegom

No 19/16, CeMMAP working papers from Institute for Fiscal Studies

Abstract: In this paper we propose a novel method to construct confidence intervals in a class of linear inverse problems. First, point estimators are obtained via a spectral cut-off method depending on a regularisation parameter, that determines the bias of the estimator. Next, the proposed confidence interval corrects for this bias by explicitly estimating it based on a second regularisation parameter ρ, which is asymptotically smaller than α. The coverage error of the interval is shown to converge to zero. The proposed method is illustrated via two simulation studies, one in the context of functional linear regression, and the second one in the context of instrumental regression.

Date: 2016-05-09
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.cemmap.ac.uk/wp-content/uploads/2020/08/CWP1916.pdf (application/pdf)

Related works:
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:azt:cemmap:19/16

DOI: 10.1920/wp.cem.2016.1916

Access Statistics for this paper

More papers in CeMMAP working papers from Institute for Fiscal Studies Contact information at EDIRC.
Bibliographic data for series maintained by Dermot Watson ().