辽宁石油化工大学学报

辽宁石油化工大学学报 ›› 2016, Vol. 36 ›› Issue (5): 74-77.DOI: 10.3969/j.issn.1672-6952.2016.05.016

• 经济管理 • 上一篇    下一篇

学习型预期与适应性学习算法研究

战 颂,丁洪福   

  1. 辽宁石油化工大学经济管理学院,辽宁抚顺113001
  • 收稿日期:2015-05-27 修回日期:2015-07-08 出版日期:2016-10-20 发布日期:2016-10-29
  • 作者简介:战颂(1978-),女,博士研究生,讲师,从事应用统计、政治关联等方面的研究;E-mail:songsong2_2000@163.com。
  • 基金资助:
    辽宁省社会科学规划基金项目(L14BJY028)。

Adaptive Learning Expectation and the Research of Adaptive Learning Algorithm

Zhan Song Ding Hongfu   

  1. School of Economics & Management,Liaoning Shihua University,Fushun Liaoning 113001,China
  • Received:2015-05-27 Revised:2015-07-08 Published:2016-10-20 Online:2016-10-29

摘要: 适应性学习作为理性预期的替代,正在广泛应用于宏观经济模型的构建。适应性学习假定经济行为主体能够像计量经济学家一样,运用统计或计量经济模型,去形成自己的预期。然而,预期的准确性与适应性学习算法密切相关。通过查阅适应性学习相关文献,对现有文献中常用的适应性学习算法进行梳理,并基于可变遗忘因子的最小二乘算法对两种常用适应性学习算法进行了理论推导,以期为以后的研究提供一定的借鉴。

关键词: 学习型预期, 适应性学习算法, 可变遗忘因子最小二乘算法

Abstract:

As an alternative to rational expectations, adaptive learning was widely used to build macroeconomic models. Adaptive learning could be assumed that economic agents like econometricians could use statistical or econometric models to form their own expectations. However, the accuracy of expectation was closely related to the adaptive learning algorithm. The adaptive learning literature was referred and disentangled, and based on least squares algorithm of a derived variable forgetting factor, the theoretical derivation of two common adaptive learning algorithms was proposed, in order to provide some reference for the future research.

Key words: Learning expectation, Adaptive learning algorithm, Variable forgetting-factor least squares algorithm

引用本文

战 颂,丁洪福. 学习型预期与适应性学习算法研究[J]. 辽宁石油化工大学学报, 2016, 36(5): 74-77.

Zhan Song, Ding Hongfu.

Adaptive Learning Expectation and the Research of Adaptive Learning Algorithm[J]. Journal of Liaoning Petrochemical University, 2016, 36(5): 74-77.

使用本文

0
    /   /   推荐

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://journal.lnpu.edu.cn/CN/10.3969/j.issn.1672-6952.2016.05.016

               http://journal.lnpu.edu.cn/CN/Y2016/V36/I5/74