SSE/EFI Working Paper Series in Economics and Finance
No 637:
Modelling autoregressive processes with a shifting mean
Andrés González ()
and Timo Teräsvirta ()
Abstract: In this paper we introduce an autoregressive model with a
deterministically shifting intercept. This implies that the model has a
shifting mean and is thus nonstationary but stationary around a nonlinear
deterministic component. The shifting intercept is defined as a linear
combination of logistic transition functions with time as the transition
variables. The number of transition functions is determined by selecting
the appropriate functions from a possibly large set of alternatives using a
sequence of specification tests. This selection procedure is a modification
of a similar technique developed for neural network modelling by White
(2006). A Monte Carlo experiment is conducted to show how the proposed
modelling procedure and some of its variants work in practice. The paper
contains two applications in which the results are compared with what is
obtained by assuming that the time series used as examples may contain
structural breaks instead of smooth transitions and selecting the number of
breaks following the technique of Bai and Perron (1998).
Keywords: deterministic shift; nonlinear autoregression; nonstationarity; nonlinear trend; smooth transition; structural change; (follow links to similar papers)
JEL-Codes: C22; C52; (follow links to similar papers)
26 pages, September 27, 2006, Revised May 22, 2007
This is the revised (May 2007) version of the paper.
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- This paper is published as:
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González, Andrés and Timo Teräsvirta, (2008), 'Modelling autoregressive processes with a shifting mean', Studies in Nonlinear Dynamics and Econometrics, Vol. 12, No. No. 1, Article 1
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