A Monte Carlo study of the forecasting performance of empirical SETAR models
通过蒙特卡洛模拟，比较了多种实证SETAR模型与线性自回归模型在多步预测中的表现，发现非线性模型在某些状态下有优势，但并非普遍。
In this paper we investigate the multi-period forecast performance of a number of empirical selfexciting threshold autoregressive (SETAR) models that have been proposed in the literature for modelling exchange rates and GNP, amongst other variables. We take each of the empirical SETAR models in turn as the DGP to ensure that the ‘non-linearity’ characterises the future, and compare the forecast performance of SETAR and linear autoregressive models on a number of quantitative and qualitative criteria. Our results indicate that non-linear models have an edge in certain states of nature but not in others, and that this can be highlighted by evaluating forecasts conditional upon the regime.