用神经网络模拟平滑转换自回归模型，提升经济金融预测精度。
STAN: Smooth Transition Autoregressive Networks
- 将STAR模型类比为多层神经网络，用隐藏层和激活函数实现平滑状态切换。
- 通过多层结构捕捉非线性关系，比传统STAR模型更灵活可扩展。
- 适合需要建模复杂经济动态的金融预测研究者使用。
传统平滑转换自回归（STAR）模型通过特定转换变量实现平滑的状态变化，有效建模动态系统。本文提出一种新方法，将STAR模型与多层神经网络架构类比。所提出的神经网络结构模仿STAR框架，利用多层结构模拟状态间的平滑过渡，并捕捉复杂的非线性关系。网络的隐藏层与激活函数设计旨在复现STAR模型典型的渐进切换行为，从而提供一种更灵活、可扩展的制度依赖建模方式。研究表明，神经网络可作为STAR模型的强大替代方案，在经济与金融预测中具有提升预测准确性的潜力。
原文摘要 · Abstract (English)
Traditional Smooth Transition Autoregressive (STAR) models offer an effective way to model these dynamics through smooth regime changes based on specific transition variables. In this paper, we propose a novel approach by drawing an analogy between STAR models and a multilayer neural network architecture. Our proposed neural network architecture mimics the STAR framework, employing multiple layers to simulate the smooth transition between regimes and capturing complex, nonlinear relationships. The network's hidden layers and activation functions are structured to replicate the gradual switching behavior typical of STAR models, allowing for a more flexible and scalable approach to regime-dependent modeling. This research suggests that neural networks can provide a powerful alternative to STAR models, with the potential to enhance predictive accuracy in economic and financial forecasting.
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