Genetic Algorithm Learning in a New Keynesian Macroeconomic Setup

Cars Hommes, Tomasz Makarewicz, Domenico Massaro and T. Smits
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T. Smits: SEO Economic Research

No 15-01, CeNDEF Working Papers from Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance

Abstract: In order to understand heterogeneous behaviour amongst agents, empirical data from Learning-to-Forecast (LtF) experiments can be used to construct learning models. This paper follows up on Assenza et al. (2013) by using a genetic algorithms (GA) model to replicate the results from their LtF experiment. In this GA model individuals optimise an adaptive, a trend following and an anchor coefficient in a population of general prediction heuristics. We replicate experimental treatments in a New-Keynesian environment with increasing complexity and use Monte Carlo simulations to investigate how well the model explains the experimental data. We find that the model is able to replicate the three different types of behaviour in the treatments using one GA model. The research furthermore shows that heterogeneous behaviour can be explained by an adaptive, anchor and trend extrapolating component and therewith contributes to the existing literature in the way that GA can be used to explain heterogeneous behaviour in LtF experiments with different types of complexity.

Date: 2015
New Economics Papers: this item is included in nep-cbe and nep-cmp
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Citations: View citations in EconPapers (1)

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Journal Article: Genetic algorithm learning in a New Keynesian macroeconomic setup (2017)
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