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Block bootstrap for dependent errors-in-variables

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  • Michal Pešta

Abstract

A linear errors-in-variables (EIV) model that contains measurement errors in the input and output data is considered. Weakly dependent (α- and ϕ-mixing) errors, not necessarily stationary nor identically distributed, are taken into account within the EIV model. Parameters of the EIV model are estimated by the total least squares approach, which provides highly non linear estimates. Because of this, many statistical procedures for constructing confidence intervals and testing hypotheses cannot be applied. One possible solution to this dilemma is a block bootstrap. An appropriate moving block bootstrap procedure is provided and its correctness proved. The results are illustrated through a simulation study and applied on real data as well.

Suggested Citation

  • Michal Pešta, 2017. "Block bootstrap for dependent errors-in-variables," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(4), pages 1871-1897, February.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:4:p:1871-1897
    DOI: 10.1080/03610926.2015.1030423
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