Modelling Non-Linear Relationships Between Long-Memory Variables

该项目开发了估算“长记忆”过程非线性模型的新程序，即长时间内数值之间的关系。这些新方法可以应用于宏观经济学、金融经济学和劳动经济学的许多问题。它们对于模拟宏观经济和天气变量之间的关系尤其重要，例如在研究全球变暖时，因为这些变量肯定是缓慢移动的，而且它们之间的关系很可能是非线性的。关于长记忆过程的研究在很大程度上是线性的。有许多非线性建模技术可用于近似实际关系，但这些程序通常或明确或隐含地假设变量是平稳的或短记忆的。为了提供一类适合为长变量之间的非线性关系建模的技术，本项目将在非线性框架中制定长记忆的新定义和测试，并指定用于测试线性和克服某些技术的“数据缺失”特性的新技术。

This project develops new procedures for estimating non-linear models of "long-memory" processes, that is relationships between values over long periods. These new procedures can be applied to many questions in macro, financial and labor economics. They are particularly relevant for modelling relationships between macroeconomic and weather variables, as when studying global warming for instance, as these variables are certainly slowly moving and the relationships are likely to be non-linear. Work concerning long memory processes has largely been linear. There are many non-linear modelling techniques available to approximate actual relationships but these procedures usually assume, either explicitly or implicitly, that the variables are stationary, or short-memory. In order to provide a class of techniques appropriate for modelling non-linear relationships between long variables, this project will develop new definitions and tests of long-memory in the non-linear framework and specify new techniques for testing for linearity and for overcoming the "data-missing" properties of some of the techniques.