Forecasting in a complex environment: Machine learning sales expectations in a Stock Flow Consistent Agent-Based simulation model
Ermanno Catullo (),
Mauro Gallegati () and
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Ermanno Catullo: Research Department, Link Campus University, Rome, Italy
No 2020/17, Working Papers from Economics Department, Universitat Jaume I, Castellón (Spain)
The aim of this paper is to investigate how different degrees of sophistication in agents’ behavioural rules may affect individual and macroeconomic performances. In particular, we analyze the effects of introducing into an agentbased macro model firms that are able to formulate effective sales forecasts by using machine learning. 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)
Pages: 33 pages
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for, nep-hme, nep-mac and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:jau:wpaper:2020/17
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