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HETEROGENEITY IN INFLATION EXPECTATIONS AND MACROECONOMIC DYNAMICS UNDER EVOLUTIONARILY SATISFICING LEARNING

Published online by Cambridge University Press:  29 June 2020

Jaylson Jair da Silveira  and
Gilberto Tadeu Lima
Jaylson Jair da Silveira
Affiliation:
Federal University of Santa Catarina, Brazil
Gilberto Tadeu Lima*
Affiliation:
University of São Paulo, Brazil
*
Address correspondence to: Gilberto Tadeu Lima, University of São Paulo, Av. Prof. Luciano Gualberto 908, 05508-010 São Paulo - SP, Brazil. e-mail giltadeu@usp.br. Phone: + 55 11 3091 5907.
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Abstract

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Drawing on the empirical evidence that heterogeneity in inflation expectations is persistent and endogenously time-varying, we embed two inflation forecasting strategies—one based on costly ex ante perfect foresight, and the second based on costless ex ante extrapolative trend-following—in a macrodynamic model. Drawing also on the empirical evidence that inflation forecast errors may have to exceed some threshold before agents abandon their previously selected forecasting strategy, we describe agents as switching between forecasting strategies according to evolutionarily satisficing learning. Convergence to a long-run equilibrium consistent with output growth, unemployment and inflation at their natural levels may be achieved even if heterogeneity in inflation forecasting strategies (with predominance of the extrapolative foresight strategy) is an attractor of an evolutionarily satisficing dynamic perturbed by mutant agents. Thus, in keeping with the empirical evidence, heterogeneity in strategies to form inflation expectations (with prevalence of bounded rationality) can be a stable long-run equilibrium.

Keywords

Heterogeneous Inflation ExpectationsPerfect ForesightExtrapolative ForesightSatisficing Learning Dynamics

Information

Type
Articles
Information
Macroeconomic Dynamics , Volume 26 , Issue 2 , March 2022 , pp. 361 - 393
Copyright
© Cambridge University Press 2020

Footnotes

We are grateful to Larry Samuelson, William Brock, two anonymous referees and the Associate Editor for helpful comments and suggestions. Nonetheless, we take full responsibility for any remaining errors. This work was supported by CNPq (the Brazilian National Council of Scientific and Technological Development) [grants 312432/2018-6 (JJS) and 311811/2018-3 (GTL)]. This study was also financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nvel Superior - Brasil (CAPES) - Finance Code 001 and the São Paulo Research Foundation - Brazil (FAPESP) [grant 2019/03148-5].

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