Nonlinear autoregressive exogenous time series: structural identification via projection estimates

Author

Masry, Elias ; Tjostheim, Dag

Author_Institution

Dept. of Electr. & Comput. Eng., California Univ., San Diego, La Jolla, CA, USA

fYear

1996

fDate

24-26 Jun 1996

Firstpage

368

Lastpage

370

Abstract

We consider additive nonlinear autoregressive exogenous (ARX) time series and propose projections as means of identifying and estimating its endogenous and exogenous components. The estimates are nonparametric in nature and involve averaging of kernel type estimates. Such estimates have been treated informally in a univariate time series situation. We extend the scope to nonlinear ARX models and present a rigorous theory, including the establishment of consistency and asymptotic normality for the projection estimates

Keywords

autoregressive moving average processes; estimation theory; identification; nonlinear systems; nonparametric statistics; time series; additive time series; asymptotic normality; averaging; consistency; endogenous components; exogenous components; kernel type estimates; nonlinear autoregressive exogenous time series; nonparametric estimation; projection estimates; structural identification; univariate time series; Additives; Algorithm design and analysis; Convergence; Estimation theory; Failure analysis; Kernel; Mathematics; Time series analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistical Signal and Array Processing, 1996. Proceedings., 8th IEEE Signal Processing Workshop on (Cat. No.96TB10004

Conference_Location

Corfu

Print_ISBN

0-8186-7576-4

Type

conf

DOI

10.1109/SSAP.1996.534892

Filename

534892