Heuristic Switching Model and Exploration-Explotation Algorithm to describe long-run expectations in LtFEs: A comparison

Annarita Colasante (), Simone Alfarano and Eva Camacho-Cuena ()
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Eva Camacho-Cuena: LEE and Department of Economics, Universitat Jaume I, Castellón, Spain
Authors registered in the RePEc Author Service: Eva Camacho Cuena ()

No 2019/02, Working Papers from Economics Department, Universitat Jaume I, Castellón (Spain)

Abstract: We compare the performance of two learning algorithms in replicating individual short and long-run expectations: the Exploration-Explotation Algorithm (EEA) and the Heuristic Switching Model (HSM). Individual expectations are elicited in a series of Learning-to-Forecast Experiments (LtFEs) with different feedback mechanisms between expectations and market price: positive and negative feedback markets. We implement the EEA proposed by Colasante et al. (2018c). Moreover, we modify the existing version of the HSM in order to incorporate the long-run predictions. Although the two algorithms provide a fairly good description of marker prices in the short- run, the EEA outperforms the HSM in replicating the main characteristics of individual expectation in the long-run, both in terms of coordination of individual expectations and convergence of expectations to the fundamental value.

Keywords: Expectations; Experiment; Evolutionary Learning (search for similar items in EconPapers)
JEL-codes: C91 D03 G12 (search for similar items in EconPapers)
Pages: 45 pages
Date: 2019
New Economics Papers: this item is included in nep-cmp and nep-exp
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Related works:
Journal Article: Heuristic Switching Model and Exploration-Exploitation Algorithm to Describe Long-Run Expectations in LtFEs: a Comparison (2020)
Working Paper: Heuristic Switching Model and Exploration-Explotation Algorithm to describe long-run expectations in LtFEs: a comparison (2019)
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