Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Multi-step-ahead estimation of time series models
Authors: McElroy, Tucker
Wildi, Marc
DOI: 10.1016/j.ijforecast.2012.08.003
Published in: International Journal of Forecasting
Volume(Issue): 29
Issue: 3
Page(s): 378
Pages to: 394
Issue Date: 2013
Publisher / Ed. Institution: Elsevier
ISSN: 0169-2070
Language: English
Subject (DDC): 003: Systems
510: Mathematics
Abstract: We study the fitting of time series models via the minimization of a multi-step-ahead forecast error criterion that is based on the asymptotic average of squared forecast errors. Our objective function uses frequency domain concepts, but is formulated in the time domain, and allows the estimation of all linear processes (e.g., ARIMA and component ARIMA). By using an asymptotic form of the forecast mean squared error, we obtain a well-defined nonlinear function of the parameters that is proven to be minimized at the true parameter vector when the model is correctly specified. We derive the statistical properties of the parameter estimates, and study the asymptotic impact of model misspecification on multi-step-ahead forecasting. The method is illustrated through a forecasting exercise, applied to several time series.
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Departement: School of Engineering
Organisational Unit: Institute of Data Analysis and Process Design (IDP)
Appears in collections:Publikationen School of Engineering

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.