英文文献：Generalizing smooth transition autoregressions-推广平滑过渡自回归
英文文献作者：Emilio Zanetti Chini
英文文献摘要：
We introduce a variant of the smooth transition autoregression - the GSTAR model - capable to parametrize the asymmetry in the tails of the transition equation by using a particular generalization of the logistic function. A General-to-Specific modelling strategy is discussed in detail, with particular emphasis on two different LM-type tests for the null of symmetric adjustment towards a new regime and three diagnostic tests, whose power properties are explored via Monte Carlo experiments. Four classical real datasets illustrate the empirical properties of the GSTAR, jointly to a rolling forecasting experiment to evaluate its point and density forecasting performances. In all the cases, the dynamic asymmetry in the cycle is efficiently captured by the new model. The GSTAR beats AR and STAR competitors in point forecasting, while this superiority becomes less evident in density forecasting, specially if robust measures are considered.

我们引入了平滑过渡自回归的一个变量——GSTAR模型——能够通过使用logistic函数的特殊泛化来参数化过渡方程尾部的不对称。本文详细讨论了一种从通用到特定的建模策略，特别强调了两种不同的lm型测试，用于对新状态对称调整的零值测试和三种诊断测试，通过蒙特卡洛实验对其功率特性进行了研究。4个经典真实数据集说明了GSTAR的经验性质，并通过滚动预测实验评价了GSTAR的点和密度预测性能。在所有的情况下，周期的动态不对称被新的模型有效地捕捉。GSTAR在点预测中击败AR和STAR竞争者，而这种优势在密度预测中变得不那么明显，特别是如果考虑了稳健的措施。