Please use this identifier to cite or link to this item:
Title: Identification of threshold autoregressive moving average models
Authors: Xia, Q
Wong, H
Keywords: Arranged regression
Nonlinearity test
TMA Model
Issue Date: 2016
Publisher: Springer
Source: In WK Li, DA Stanford & H Yu (Eds.), Advances in time series methods and applications : the A. Ian McLeod festschrift, p. 195-214. New York: Springer, 2016. How to cite?
Abstract: Due to the lack of a suitablemodeling procedure and the difficulty to identify the threshold variable and estimate the threshold values, the threshold autoregressive moving average (TARMA) model with multi-regime has not attracted much attention in application. Therefore, the chief goal of our paper is to propose a simple and yet widely applicable modeling procedure for multi-regime TARMA models. Under no threshold case, we utilize extended least squares estimate (ELSE) and linear arranged regression to obtain a test statistic F, which is proved to follow an approximate F distribution. And then, based on the statistic F, we employ some scatter plots to identify the number and locations of the potential thresholds. Finally, the procedures are considered to build a TARMA model by these statistics and the Akaike information criterion (AIC). Simulation experiments and the application to a real data example demonstrate that both the power of the test statistic and the model-building can work very well in the case of TARMA models.
ISBN: 9781493965687
DOI: 10.1007/978-1-4939-6568-7_9
Appears in Collections:Book Chapter

View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

Last Week
Last month
Checked on Aug 20, 2017

Google ScholarTM



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