Forecasting in a complex environment: Machine learning sales expectations in a stock flow consistent agent-based simulation model

Ermanno Catullo, Mauro Gallegati and Alberto Russo

Journal of Economic Dynamics and Control, 2022, vol. 139, issue C

Abstract: The aim of this paper is to investigate how different degrees of sophistication in agents’ behavioral rules may affect individual and macroeconomic performances. In particular, we analyze the effects of introducing into an agent-based macro model firms that are able to formulate effective sales forecasts by using simple machine learning algorithms. These techniques are able to provide predictions that are unbiased and present a certain degree of accuracy, especially in the case of a genetic algorithm. We observe that machine learning allows firms to increase profits, though this result in a declining wage share and a smaller long-run growth rate. Moreover, the predictive methods are able to formulate expectations that remain unbiased when shocks are not massive, thus providing firms with forecasting capabilities that to a certain extent may be consistent with the Lucas Critique.

Keywords: Agent-based model; Machine learning; Genetic algorithm; Forecasting; Policy shocks (search for similar items in EconPapers)
JEL-codes: C63 D84 E32 E37 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Working Paper: Forecasting in a complex environment: Machine learning sales expectations in a Stock Flow Consistent Agent-Based simulation model (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:139:y:2022:i:c:s0165188922001117

DOI: 10.1016/j.jedc.2022.104405

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