On forecasting SETAR processes

Jan G. Gooijer and Paul T. De Bruin

Statistics & Probability Letters, 1998, vol. 37, issue 1, 7-14

Abstract: Suppose a time series {Yt} is generated by a first-order stationary self-exciting threshold autoregressive (SETAR) model with Gaussian innovations. The minimum mean squared error h-step ahead forecast for h> 2 involves a sequence of complicated numerical integrations and closed-form expressions are very difficult or even impossible to obtain. In this paper we derive explicit approximate expressions for E[Yt+hYs; s [less-than-or-equals, slant] t] and Var[Yt+hYs; s [less-than-or-equals, slant] t] (h> 2) for various SETAR models. The approximations are reasonably accurate as compared with alternative methods based on numerical integration and Monte Carlo experiments.

Keywords: Nonlinear; time; series; Monte; Carlo; Normality; Self-exciting; threshold; autoregressive; process (search for similar items in EconPapers)
Date: 1998
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Citations: View citations in EconPapers (19)

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