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Genetic algorithm learning in a New Keynesian macroeconomic setup

Cars Hommes (), Tomasz Makarewicz, Domenico Massaro and Tom Smits
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Tom Smits: SEO Amsterdam Economics

Journal of Evolutionary Economics, 2017, vol. 27, issue 5, 1133-1155

Abstract: Abstract In order to understand heterogeneous behavior 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 optimize 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 evolutionary learning model is able to replicate the three different types of behavior, i.e. convergence to steady state, stable oscillations and dampened oscillations in the treatments using one GA model. Heterogeneous behavior can thus be explained by an adaptive, anchor and trend extrapolating component and the GA model can be used to explain heterogeneous behavior in LtF experiments with different types of complexity.

Keywords: Expectation formation; Learning to forecast experiment; Genetic algorithm model of individual learning (search for similar items in EconPapers)
JEL-codes: C53 C61 C63 C92 E12 E31 E52 (search for similar items in EconPapers)
Date: 2017
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Working Paper: Genetic Algorithm Learning in a New Keynesian Macroeconomic Setup (2015) Downloads
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