Nonparametric detection of change in the slope and intercept in linear structural errors-in-variables models

Yuliya V. Martsynyuk

Statistics & Probability Letters, 2021, vol. 169, issue C

Abstract: This paper deals with two generalizations of the classical linear structural errors-in-variables model (SEIVM) with univariate observations. In these two models, the data may no longer come from a single linear SEIVM, due to that a change in the slope in the first model and a change in the intercept in the second model may occur after observing the first k∗ data pairs, where k∗ is assumed to be unknown, while the explanatory variables as well as vectors of the measurement errors are independent and identically distributed. Nonparametric asymptotic tests are developed to detect a possible change in each of these SEIVM’s, assuming the existence of four moments for the explanatory and error variables. It appears that the SEIVM with a possible change in the slope of this paper has not been considered in the literature, while the SEIVM with a possible change in the intercept has not been studied before nonparametrically.

Keywords: Linear structural errors-in-variables model; Nonparametric test for change in the slope; Nonparametric test for change in the intercept; Weighted tied-down partial sums process (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1016/j.spl.2020.108957

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