正交级数方法与非平稳时间序列模型 估计和检验的一些研究进展

There are considerable nonstationary time series in economics, finance, climate science and related areas. In last two decades or so, in order to improve theoretical research in these disciplines, asymptotic theory on nonstationary time series has captured close attention and well developed; on the other hand, classical series estimation often requires the values of variables considered fall into a bounded compact interval that in some circumstance suppresses the development and application of the method in nonparametric context, especially in the present of nonstationary time series. In order to break through the bottleneck of the conventional sieve method, the authors and their coauthors use orthogonal series expansion to achieve some theoretical results and their applications, in particular in nonparametric and nonstationary time series. These studies lay a foundation for the use of the series estimation in economics, finance, climate science and related disciplines.