Dynamic partially functional linear regression model

Jiang Du (), Hui Zhao and Zhongzhan Zhang
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Jiang Du: Beijing University of Technology
Hui Zhao: Beijing University of Technology
Zhongzhan Zhang: Beijing University of Technology

Statistical Methods & Applications, 2019, vol. 28, issue 4, No 5, 679-693

Abstract: Abstract In this paper, we develop a dynamic partially functional linear regression model in which the functional dependent variable is explained by the first order lagged functional observation and a finite number of real-valued variables. The bivariate slope function is estimated by bivariate tensor-product B-splines. Under some regularity conditions, the large sample properties of the proposed estimators are established. We investigate the finite sample performance of the proposed methods via Monte Carlo simulation studies, and illustrate its usefulness by the analysis of the electricity consumption data.

Keywords: Functional time series; Bivariate tensor-product B-splines; Functional data analysis; Autoregressive Hilbertian process; 62G08; 62G20 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10260-019-00457-x

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