Computational Economics https://doi.org/10.1007/s10614-019-09959-y Heterogeneous Expectations and Uncertain Inflation Target Stefano Marzioni 1 · Guido Traficante 2 Accepted: 3 December 2019 © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract We analyze a new Keynesian economy populated by adaptive-learning agents with heterogeneous beliefs about the time-varying inflation target. A fraction of agents is assumed to have a full and updated information set including the permanent and tem- porary component of the inflation target at the current period, while the remainder of agents receives a signal and use it to estimate the target components solving a Kalman filter problem. The proportion of the two strategies is endogenous and depends on a measure of past performance of predictors. We conduct stochastic simulations to assess whether different hypotheses about the information regime may affect macroe- conomic stability in the short and in the long run. We find that a smaller proportion of agents using costly information is associated to larger expected losses. Nevertheless, the fraction of agents following this strategy drops signficantly in the aftermath of a shock to the inflation target because the Kalman signal extraction procedure allows to follow more closely the actual dynamics of the economy. Keywords Kalman filter · Adaptive learning · Policy targets JEL Classification E52 · C62 · D83 · D84 We would like to thank the participants at 22nd Workshop on the Economic Science with Heterogeneous Interacting Agents at “Catholic University”—Milan. We acknowledge financial support from the Italian Ministry of University and Research (PRIN 2015 “The Architecture of Markets and Institutions after the Crisis: Theoretical Foundations and Policy Implications”). Usual disclaimers apply. B Guido Traficante guido.traficante@unier.it Stefano Marzioni stefano.marzioni@unicusano.it 1 Niccolò Cusano University, Via Don Gnocchi 3, 00166 Rome, Italy 2 European University of Rome, Via degli Aldobrandeschi 190, 00163 Rome, Italy 123