{"title":"Expectations, learning gains, and forecast errors: Assessing nonlinearities with a functional coefficient approach","authors":"Fabio Milani","doi":"10.1016/j.econlet.2025.112612","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div><div>I use data on professional survey forecasts to estimate nonparametric functional-coefficient regression models.</div><div>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.</div></div>","PeriodicalId":11468,"journal":{"name":"Economics Letters","volume":"256 ","pages":"Article 112612"},"PeriodicalIF":1.8000,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Economics Letters","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0165176525004495","RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECONOMICS","Score":null,"Total":0}
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.