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Long-run expectations in a learning-to-forecast experiment: a simulation approach

Annarita Colasante (), Simone Alfarano (), Eva Camacho-Cuena () and Mauro Gallegati ()
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Eva Camacho-Cuena: University Jaume I

Authors registered in the RePEc Author Service: Eva Camacho Cuena ()

Journal of Evolutionary Economics, 2020, vol. 30, issue 1, No 5, 75-116

Abstract: Abstract In this paper, we elicit short-run as well as long-run expectations on the evolution of the price of a financial asset in a Learning-to-Forecast Experiment (LtFE). Subjects, in each period, have to forecast the the asset price for each one of the remaining periods. The aim of this paper is twofold: first, we fill the gap in the experimental literature of LtFEs where great effort has been devoted to investigate short-run expectations, i.e. one step-ahead predictions, while there are no contributions that elicit long-run expectations. Second, we propose a new computational algorithm to replicate the main properties of short and long-run expectations observed in the experiment. This learning algorithm, called Exploration-Exploitation Algorithm, is based on the idea that agents anchor their expectations around the last realized price rather than on the fundamental value, with a range proportional to the past observed price volatility. When compared to the Heuristic Switching Model, our algorithm performs equally well in describing the dynamics of short-run expectations and the realized price dynamics. The EEA, additionally, is able to reproduce the dynamics long-run expectations.

Keywords: Long-run expectations; Experiment; Evolutionary learning (search for similar items in EconPapers)
JEL-codes: C91 D03 G12 (search for similar items in EconPapers)
Date: 2020
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Working Paper: Long-run expectations in a Learning-to-Forecast Experiment: A Simulation Approach (2017) Downloads
Working Paper: Long-run expectations in a Learning-to-Forecast-Experiment: a simulation approach (2017) Downloads
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DOI: 10.1007/s00191-018-0585-1

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