Abstract
Abstract Experimental evidence shows that human subjects frequently rely on adaptive heuristics to form expectations but their forecasting performance in the lab is not as inadequate as assumed in macroeconomic theory. In this paper, we use an agent-based model (ABM) to show that the average forecasting error is indeed close to zero even in a complex environment if we assume that agents augment the canonical adaptive algorithm with a Belief Correction term which takes into account the previous trend of the variable of interest. We investigate the reasons for this result using a streamlined nonlinear macro-dynamic model that captures the essence of the ABM.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
