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英文文献：Forecasting with nonlinear time series models-非线性时间序列模型的预测
英文文献作者：Anders Bredahl Kock,Timo Ter?svirta
英文文献摘要：
In this paper, nonlinear models are restricted to mean nonlinear parametric models. Several such models popular in time series econometrics are presented and some of their properties discussed. This includes two models based on universal approximators: the Kolmogorov-Gabor polynomial model and two versions of a simple artificial neural network model. Techniques for generating multi-period forecasts from nonlinear models recursively are considered, and the direct (non-recursive) method for this purpose is mentioned as well. Forecasting with complex dynamic systems, albeit less frequently applied to economic forecasting problems, is briefly highlighted. A number of large published studies comparing macroeconomic forecasts obtained using different time series models are discussed, and the paper also contains a small simulation study comparing recursive and direct forecasts in a particular case where the data-generating process is a simple artificial neural network model. Suggestions for further reading conclude the paper.
在本文中，非线性模型被限制为均值非线性参数模型。介绍了时间序列计量经济学中常用的几种模型，并讨论了它们的一些性质。这包括两个基于通用逼近器的模型:Kolmogorov-Gabor多项式模型和两个版本的简单人工神经网络模型。考虑了从非线性模型递归生成多周期预测的技术，并提出了为此目的的直接(非递归)方法。用复杂的动态系统进行预测，虽然不太经常应用于经济预测问题，但还是简单地强调。本文讨论了比较使用不同时间序列模型获得的宏观经济预测的许多已发表的大型研究，以及在数据生成过程是简单的人工神经网络模型的特定情况下比较递归预测和直接预测的小型模拟研究。文章最后总结了进一步阅读的建议。
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