Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/67150
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.
URI: http://hdl.handle.net/10397/67150
ISBN: 9781493965687
DOI: 10.1007/978-1-4939-6568-7_9
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