Initial Predictions in Learning-to-Forecast Experiment

Cees Diks () and Tomasz Makarewicz ()
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Cees Diks: CeNDEF, University of Amsterdam
Tomasz Makarewicz: CeNDEF, University of Amsterdam

Chapter Chapter 18 in Managing Market Complexity, 2012, pp 223-235 from Springer

Abstract: Abstract In this paper we estimate the distribution of the initial predictions of the Heemeijer et al. [5] Learning-to-Forecast experiment. By design, these initial predictions were uninformed. We show that in fact they have a non-continuous distribution and that they systematically under-evaluate the fundamental price. Our conclusions are based on Diks et al. [2] test which measures the proximity of two vector sets even if their underlying distributions are non-continuous.We show how this test can be used as a fitness for Genetic Algorithm optimization procedure. The resulting methodology allows for fitting non-continuous distribution into abundant empirical data and is designed for repeated experiments.

Keywords: Focal Point; Repeated Experiment; Time Path; Observation Vector; Monte Carlo Experiment (search for similar items in EconPapers)
Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnechp:978-3-642-31301-1_18

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DOI: 10.1007/978-3-642-31301-1_18

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