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

Jean-Pierre Florens, Joel L. Horowitz () and Ingrid Van Keilegom ()
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Jean-Pierre Florens: Institute for Fiscal Studies
Joel L. Horowitz: Institute for Fiscal Studies and Northwestern University

No CWP19/16, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract: In this paper we propose a novel method to construct confi dence 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 con fidence interval corrects for this bias by explicitly estimating it based on a second regularisation parameter ?, which is asymptotically smaller than a. 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.

Keywords: Bias-correction; functional linear regression; instrumental regression; inverse problem; regularisation; spectral cut-o ff (search for similar items in EconPapers)
Date: 2016-05-09
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Bias-Corrected Confidence Intervals in a Class of Linear Inverse Problems (2017)
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