Identification and estimation in quantile varying-coefficient models with unknown link function

• Lili Yue ^[1] ; Gaorong Li ^[1] ; Heng Lian ^[2]
  1. [1] Beijing University of Technology

     Beijing University of Technology

     China

     
  2. [2] City University of Hong Kong

     City University of Hong Kong

     RAE de Hong Kong (China)

     
• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 28, Nº. 4, 2019, págs. 1251-1275
• Idioma: inglés
• DOI: 10.1007/s11749-019-00638-6
• Texto completo no disponible (Saber más ...)
• Resumen

  • In this paper, we consider the estimation problem of quantile varying-coefficient models when the link function is unspecified, which significantly expands the existing works on varying-coefficient models with unspecified link function focusing only on mean regression. We provide new identification conditions which are weaker than existing ones. Under these identification conditions, we use polynomial splines to estimate both the varying coefficients and the link functions and establish the convergence rate of the estimator. Our simulation studies and a real data application illustrate the finite sample performance of the estimators.