Initial Predictions in Learning-to-Forecast Experiment

  • Cees Diks
  • Tomasz Makarewicz
Part of the Lecture Notes in Economics and Mathematical Systems book series (LNE, volume 662)


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.


Focal Point Repeated Experiment Time Path Observation Vector Monte Carlo Experiment 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.CeNDEF, University of AmsterdamAmsterdamNetherlands
  2. 2.Tinbergen InstituteCeNDEF, University of AmsterdamAmsterdamNetherlands

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