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英文文献：Modeling Non-Linear Spatial Dynamics: A Family of Spatial STAR Models and an Application to U.S. Economic Growth-非线性空间动力学建模:一组空间星型模型及其在美国经济增长中的应用
英文文献作者：Pede, Valerien O.,Florax, Raymond J.G.M.,Holt, Matthew T.
英文文献摘要：
This paper investigates non-linearity in spatial processes models and allows for a gradual regime-switching structure in the form of a smooth transition autoregressive process. Until now, applications of the smooth transition autoregressive (STAR) model have been largely confined to the time series context. The paper focuses on extending the non-linear smooth transition perspective to spatial processes models, in which spatial correlation is taken into account through the use of a so-called weights matrix identifying the topology of the spatial system. We start by deriving a non-linearity test for a simple spatial model, in which spatial correlation is only included in the transition function. Next, we propose a non-linearity test for a model that includes a spatially lagged dependent variable or spatially autocorrelated innovations as well. Monte Carlo simulations of the various test statistics are performed to examine their power and size. The proposed modeling framework is then used to identify convergence clubs in the context of U.S. county-level economic growth over the period 1963–2003.
本文研究了空间过程模型中的非线性，并允许以平滑过渡自回归过程的形式出现一个渐进的政权转换结构。到目前为止，平滑过渡自回归(STAR)模型的应用在很大程度上局限于时间序列背景。本文着重于扩展非线性平滑过渡视角到空间过程模型，在空间过程模型中，通过使用所谓的权重矩阵识别空间系统的拓扑来考虑空间相关性。我们首先对一个简单的空间模型进行非线性检验，其中空间相关性只包含在过渡函数中。接下来，我们提出了一个模型的非线性检验，包括一个空间滞后的因变量或空间自相关创新以及。对各种测试统计量进行蒙特卡洛模拟，以检验其功率和大小。然后，使用所提议的建模框架来确定1963-2003年期间美国县级经济增长的趋同俱乐部。
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