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

M. D. Ruiz-Medina (), D. Miranda and R. M. Espejo
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M. D. Ruiz-Medina: O.R. University of Granada
D. Miranda: O.R. University of Granada
R. M. Espejo: O.R. University of Granada

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2019, vol. 28, issue 3, No 18, 943-968

Abstract: 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.

Keywords: ARH(1) errors; Dynamical functional multiple regression; Firm leverage maps; Generalized least-squared estimator; Kernel regressors; 60G25; 60G60; 62J05; 62J10 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s11749-018-0614-2

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