Dynamical multiple regression in function spaces, under kernel regressors, with ARH(1) errors
Abstract
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.
Cite
@article{arxiv.1808.01655,
title = {Dynamical multiple regression in function spaces, under kernel regressors, with ARH(1) errors},
author = {M. D. Ruiz-Medina and D. Miranda and R. M. Espejo},
journal= {arXiv preprint arXiv:1808.01655},
year = {2018}
}
Comments
This paper has been submitted to TEST Journal, and now the reviewing process status is on the submitted first revised version on June, 2018