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Learning, Forecasting and Optimizing: An Experimental Study

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  • Te Bao

    (University of Amsterdam)

  • John Duffy

    (University of Pittsburgh)

  • Cars Hommes

    (University of Amsterdam)

Abstract

Rational Expectations (RE) models have two crucial dimensions: 1) agents correctly forecast future prices given all available information, and 2) given expectations, agents solve optimization problems and these solutions in turn determine actual price realizations. Experimental testing of such models typically focuses on only one of these two dimensions. In this paper we consider both forecasting and optimization decisions in an experimental cobweb economy. We report results from four experimental treatments: 1) subjects form forecasts only, 2) subjects determine quantity only (solve an optimization problem), 3) they do both and 4) they are paired in teams and one member is assigned the forecasting role while the other is assigned the optimization task. All treatments converge to Rational Expectation Equilibrium (REE), but at very different speeds. We observe that performance is the best in treatment 1) and worst in the treatment 3). Most forecasters use a n adaptive expectations rule. Subjects are less likely to make conditionally optimal production decision for given forecasts in treatment 3) where the forecast is made by themselves, than in treatment 4) where the forecast is made by the other member of their team, which suggests that "two heads are better than one" in finding REE.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 12-015/1.

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Date of creation: 17 Feb 2012
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Handle: RePEc:dgr:uvatin:20120015

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Web page: http://www.tinbergen.nl

Related research

Keywords: Learning; Rational Expectations; Optimization; Experimental Economics; Bounded Rationality;

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References

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Citations

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Cited by:
  1. Bao, Te & Duffy, John, 2014. "Adaptive vs. eductive learning: Theory and evidence," Research Report 14002-EEF, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
  2. Bao, T. & Hommes, C.H. & Makarewicz, T.A., 2014. "Bubble Formation and (In)efficient Markets in Learning-to-Forecast and -Optimize Experiments," CeNDEF Working Papers, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance 14-01, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
  3. Wolfgang Luhan & Johann Scharler, 2013. "Monetary Policy, Inflation Illusion and the Taylor Principle: An Experimental Study," Working Papers, Faculty of Economics and Statistics, University of Innsbruck 2013-03, Faculty of Economics and Statistics, University of Innsbruck.
  4. Jasmina Arifovic & George Evans & Olena Kostyshyna, 2013. "Are Sunspots Learnable? An Experimental Investigation in a Simple General-Equilibrium Model," Working Papers, Bank of Canada 13-14, Bank of Canada.

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