强个体效应因子模型ER方法的机器学习改进
发布时间：2018-09-01 16:05
【摘要】：本文通过对强个体效应近似因子模型ER方法的再理解,尝试利用机器学习方法对ER法进行改进,尝试寻找其改进算法解决ER方法在强个体情况下失效的情况,并与已经提出的利用有界单调映射方法进行比较,得到了在改进估计结果的同时对个体效应强度识别能力较强的因子个数估计方法。在文章的叙述过程中,不仅提供解决方法,还以解决问题的思路为顺序进行叙述,并关注算法的实现与优化。
[Abstract]:In this paper, we try to improve the ER method by using machine learning method through the reunderstanding of the ER method of the strong individual effect approximate factor model, and try to find its improved algorithm to solve the failure of the ER method in the strong individual case. Compared with the proposed method of bounded monotone mapping, a new method for estimating the number of factors is obtained, which not only improves the estimation results, but also has a better ability to identify the individual effect intensity. In the course of the narration, not only the solution method is provided, but also the idea of solving the problem is described in order, and the realization and optimization of the algorithm are paid attention to.
【学位授予单位】：浙江工商大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP181
，
本文编号：2217689
[Abstract]:In this paper, we try to improve the ER method by using machine learning method through the reunderstanding of the ER method of the strong individual effect approximate factor model, and try to find its improved algorithm to solve the failure of the ER method in the strong individual case. Compared with the proposed method of bounded monotone mapping, a new method for estimating the number of factors is obtained, which not only improves the estimation results, but also has a better ability to identify the individual effect intensity. In the course of the narration, not only the solution method is provided, but also the idea of solving the problem is described in order, and the realization and optimization of the algorithm are paid attention to.
【学位授予单位】：浙江工商大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP181
，
本文编号：2217689
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