Abstract
In the absence of any trend component, the observed series y t would be completely characterised by the cycle (since y t = α + ɛ t ) and thus, in general, could be represented by an ARMA model of the form (2.30). The observed series would thus be stationary. We now consider modelling time series that contain stochastic trend components and which are therefore generally referred to as nonstationary processes.
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© 2003 Terence C. Mills
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Mills, T.C. (2003). Stochastic Trends and Cycles. In: Modelling Trends and Cycles in Economic Time Series. Palgrave Texts in Econometrics. Palgrave Macmillan, London. https://doi.org/10.1057/9780230595521_3
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DOI: https://doi.org/10.1057/9780230595521_3
Publisher Name: Palgrave Macmillan, London
Print ISBN: 978-1-4039-0209-2
Online ISBN: 978-0-230-59552-1
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