Modelling Non-Linear Relationships Between Long-Memory Variables

这个项目开发了新的程序来估计非线性 “长记忆”过程的模型，即 长期的价值观。 这些新程序可适用于 宏观经济、金融和劳动经济学的许多问题。 他们是 特别适用于模拟 宏观经济和天气变量，如研究全球 例如，变暖，因为这些变量肯定是缓慢的， 移动，并且关系可能是非线性的。 关于长记忆过程的研究主要是线性的。 有许多非线性建模技术可用于 近似的实际关系，但这些程序通常 假设，无论是显式或隐式，变量是 静止的或短暂的记忆。 为了提供一类 适用于非线性关系建模的技术 在长变量之间，该项目将开发新的定义 并在非线性框架下进行长记忆测试，并指定 测试线性和克服 一些技术的“数据缺失”特性。

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.