Dynamical multiple regression in function spaces, under kernel regressors, with ARH(1) errors

• M. D. Ruiz-Medina ^[1] ; D. Miranda ^[1] ; R. M. Espejo ^[1]
  1. [1] Universidad de Granada

     Universidad de Granada

     Granada, España

     
• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 28, Nº. 3, 2019, págs. 943-968
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • A linear multiple regression model in function spaces is formulated, under temporal correlated errors. This formulation involves kernel regressors. A generalized least-squared regression parameter estimator is derived. Its asymptotic normality and strong consistency is obtained, under suitable conditions. The correlation analysis is based on a componentwise estimator of the residual autocorrelation operator. When the dependence structure of the functional error term is unknown, a plug-in generalized least-squared regression parameter estimator is formulated. Its strong consistency is proved as well. A simulation study is undertaken to illustrate the performance of the presented approach, under different regularity conditions. An application to financial panel data is also considered