Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/149765 
Year of Publication: 
2016
Series/Report no.: 
cemmap working paper No. CWP19/16
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
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 p, which is asymptotically smaller than ». 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.
Subjects: 
Bias-correction
functional linear regression
instrumental regression
inverse problem
regularisation
spectral cut-off
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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