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

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