Expectations, learning gains, and forecast errors: Assessing nonlinearities with a functional coefficient approach

IF 1.8 4区 经济学 Q2 ECONOMICS
Fabio Milani
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引用次数: 0

Abstract

This paper investigates potential nonlinearities in the gain function, which, under adaptive learning, regulates the updating of agents’ beliefs in response to recent forecast errors.
I use data on professional survey forecasts to estimate nonparametric functional-coefficient regression models.
The estimation results reveal nonlinearities in the relationships between expectations and forecast errors, which are indicative of nonlinear gain functions. Gains increase when forecast errors are historically large, and respond asymmetrically to past overpredictions and underpredictions. The findings suggest incorporating nonlinearities in the modeling of learning gains, instead of relying on the constant-gain assumption.
期望、学习增益和预测误差:用函数系数方法评估非线性
本文研究了增益函数中潜在的非线性,在自适应学习下,增益函数根据最近的预测误差调节智能体信念的更新。我使用专业调查预测的数据来估计非参数函数系数回归模型。估计结果显示期望和预测误差之间存在非线性关系,这表明增益函数是非线性的。当预测误差在历史上较大时,收益增加,并且对过去的高估和低估作出不对称的反应。研究结果建议在学习增益的建模中加入非线性,而不是依赖于恒定增益的假设。
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来源期刊
Economics Letters
Economics Letters ECONOMICS-
CiteScore
3.20
自引率
5.00%
发文量
348
审稿时长
30 days
期刊介绍: Many economists today are concerned by the proliferation of journals and the concomitant labyrinth of research to be conquered in order to reach the specific information they require. To combat this tendency, Economics Letters has been conceived and designed outside the realm of the traditional economics journal. As a Letters Journal, it consists of concise communications (letters) that provide a means of rapid and efficient dissemination of new results, models and methods in all fields of economic research.
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