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A restricted Liu estimator for binary regression models and its application to an applied demand system

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  • Kristofer Månsson
  • B.M. Golam Kibria
  • Ghazi Shukur

Abstract

In this article, we propose a restricted Liu regression estimator (RLRE) for estimating the parameter vector, β , in the presence of multicollinearity, when the dependent variable is binary and it is suspected that β may belong to a linear subspace defined by Rβ = r . First, we investigate the mean squared error (MSE) properties of the new estimator and compare them with those of the restricted maximum likelihood estimator (RMLE). Then we suggest some estimators of the shrinkage parameter, and a simulation study is conducted to compare the performance of the different estimators. Finally, we show the benefit of using RLRE instead of RMLE when estimating how changes in price affect consumer demand for a specific product.

Suggested Citation

  • Kristofer Månsson & B.M. Golam Kibria & Ghazi Shukur, 2016. "A restricted Liu estimator for binary regression models and its application to an applied demand system," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(6), pages 1119-1127, May.
    • Handle: RePEc:taf:japsta:v:43:y:2016:i:6:p:1119-1127
    DOI: 10.1080/02664763.2015.1092110
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    Cited by:

    1. Kristofer Månsson & B. M. Golam Kibria, 2021. "Estimating the Unrestricted and Restricted Liu Estimators for the Poisson Regression Model: Method and Application," Computational Economics, Springer;Society for Computational Economics, vol. 58(2), pages 311-326, August.

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