Forecasting macroeconomic fundamentals in economic crises

Maurizio Bovi () and Roy Cerqueti

Annals of Operations Research, 2016, vol. 247, issue 2, No 4, 469 pages

Abstract: Abstract The paper studies the way economic turmoils influence the lay agents’ predictions of macroeconomic fundamentals. The recent economic crises have, in fact, led several authors to challenge the standard macroeconomic view that all agents are Muth-rational, hence omniscient and homogeneous, forecasters. In this paper lay agents are assumed to be heterogeneous in their predictive ability. Heterogeneity is modeled by assuming that people have equal loss functions, but different asymmetry parameters. The adopted methodological tools are grounded in the standard operational research theory. Specifically, we develop a dynamic stochastic optimization problem, which is solved by performing extensive Monte Carlo simulations. Results show that the less sophisticated forecasters in our setting—the medians—never perform as muthians and that second best (SB) agents do that only occasionally. This regardless the size of the crisis. Thus, as in the real world, in our artificial economy heterogeneity is a structural trait. More intriguingly, simulations also show that the medians’ behavior tend to be relatively smoother than that of SB agents, and that the difference between them widens in the case very serious crises. In particular, great recessions make SB agents’ predictions relatively more biased. An explanation is that dramatic crises extend the available information set (e.g., due to greater mass media coverage), and this leads SB agents, who are more attentive to revise their forecasts than medians. The point is that more information does not necessarily mean better forecasting performances. All considered, thus, our simulations suggest a rewording of Ackoff’s famous phrase: it is not silly to not look for an optimal solution to a mess.

Keywords: Dynamic stochastic optimization model; Economic crisis; Forecasting; Heterogeneous agents; Monte Carlo simulations (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10479-015-1879-4

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