On strict stationarity and ergodicity of a non-linear ARMA model

On strict stationarity and ergodicity of a non-linear ARMA model

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Article ID: iaor19932371
Country: Israel
Volume: 29
Issue: 2
Start Page Number: 363
End Page Number: 373
Publication Date: Jun 1992
Journal: Journal of Applied Probability
Authors: ,
Keywords: time series & forecasting methods
Abstract:

Two recent papers by Petruccelli and Woolford and Chan et al. showed that the key element governing ergodicity of a threshold AR(1) model is the joint behavior of the two linear AR(1) pieces falling in the two boundary threshold regimes. They used essentially the necessary and sufficient conditions for ergodicity of a general Markov chain of Tweedie in a rather clever manner. However, it is difficult to extend the results to the more general threshold ARMA models. Besides, irreducibility is also required to apply Tweedie’s results. In this paper, instead of pursuing the ideas in Tweedie’s results, the authors shall develop a criterion similar in spirit to the technique used by Benes in the context of continuous-time Markov chains. Consequently, they derive a necessary and sufficient condition for existence of a strictly stationary solution of a general non-linear ARMA model to be introduced in Section 2 of this paper. This condition is then applied to the threshold ARMA(1,q) model to yield a sufficient condition for strict stationarity which is identical to the condition given by Petruccelli and Woolford for the threshold AR(1). Hence, the conjecture that the moving average component does not affect stationarity is partially verified. Furthermore, under an additional irreducibility assumption, ergodicity of a non-linear ARMA model is established. The paper then concludes with a necessary condition for stationarity of the threshold ARMA(1,q) model.

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