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MODELLING NONLINEAR RELATIONSHIPS BETWEEN EXTENDED-MEMORY VARIABLES

机译:扩展内存变量之间的非线性关系建模

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Many economic variables have a persistence property which may be called extended memory and the relationship between variables may well be nonlinear. This pair of properties allow for many more types of model misspecification than encountered with stationary or short-memory variables and linear relationships, and misspecifications lead to greater modelling difficulties. Examples are given using the idea of a model being balanced. Alternative definitions of extended memory are considered and a definition based on the properties of optimum forecasts is selected for later use. An important but not necessarily pervasive class of processes are those that are extended-memory but whose changes are short-memory. For this case, called 1(1), standard cointegration ideas will apply.Tests of linearity are discussed in both the 1(1) case, where a possible group of tests is easily found, and more generally. Similarly, methods of building nonlinear models based on familiar techniques, such as neural networks and projection pursuit, are briefly considered for 1(1) and the more general case. A number of areas requiring further work in this new area are emphasized.
机译:许多经济变量具有持久性,可以称为扩展内存,变量之间的关系很可能是非线性的。与固定或短内存变量和线性关系遇到的情况相比,这对属性允许更多类型的模型错误指定,错误指定导致更大的建模难度。使用平衡模型的思想给出示例。考虑扩展内存的替代定义,并选择基于最佳预测属性的定义以供以后使用。一类重要但不一定普遍的过程是那些扩展内存但其变化是短内存的过程。对于这种称为1(1)的情况,将应用标准协整思想。在1(1)情况下(很容易找到一组可能的测试)以及更广泛的情况下,都讨论了线性测试。同样,对于1(1)和更一般的情况,简要考虑了基于熟悉的技术(例如神经网络和投影追踪)构建非线性模型的方法。强调了在这个新领域中需要进一步工作的许多领域。

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