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A new confidence interval in errors-in-variables model with known error variance

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  • Liang Yan
  • Rui Wang
  • Xingzhong Xu

Abstract

This paper considers constructing a new confidence interval for the slope parameter in the structural errors-in-variables model with known error variance associated with the regressors. Existing confidence intervals are so severely affected by Gleser–Hwang effect that they are subject to have poor empirical coverage probabilities and unsatisfactory lengths. Moreover, these problems get worse with decreasing reliability ratio which also result in more frequent absence of some existing intervals. To ease these issues, this paper presents a fiducial generalized confidence interval which maintains the correct asymptotic coverage. Simulation results show that this fiducial interval is slightly conservative while often having average length comparable or shorter than the other methods. Finally, we illustrate these confidence intervals with two real data examples, and in the second example some existing intervals do not exist.

Suggested Citation

  • Liang Yan & Rui Wang & Xingzhong Xu, 2017. "A new confidence interval in errors-in-variables model with known error variance," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(12), pages 2204-2221, September.
    • Handle: RePEc:taf:japsta:v:44:y:2017:i:12:p:2204-2221
    DOI: 10.1080/02664763.2016.1247793
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    References listed on IDEAS

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    1. Jeffrey R. Thompson & Randy L. Carter, 2007. "An Overview of Normal Theory Structural Measurement Error Models," International Statistical Review, International Statistical Institute, vol. 75(2), pages 183-198, August.
    2. Hannig, Jan & Iyer, Hari & Patterson, Paul, 2006. "Fiducial Generalized Confidence Intervals," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 254-269, March.
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