Inflation targeting in a learning economy: An ABM perspective

This paper investigates the performances of an inflation targeting regime in a learning economy, whose functioning is tackled through an Agent-Based Model (ABM). While the structure of our ABM has common features with that of the New Keynesian canonical modelling framework, we model individual agents\' forms of behaviour under procedural rationality in the sense of Simon (1971). Instead of assuming that they fully optimize on an intertemporal basis beforehand, and make use of rational expectations in that respect, agents are supposed to adopt economic forms of behaviour that are guided by simple rules of thumb -- or heuristics -- while a continuous learning process governs the evolution of those simple rules. Departures from the rational expectations equilibrium endogenously arise from those learning rules. Subsequently, the central bank implements an inflation targeting regime via a monetary policy rule. Our aim is to analyse the interplay between the learning mechanisms operating at the individual level, and the features and performances of the inflation targeting regime. In such a setting, we show the primary importance of the credibility of central bank announcements regarding macroeconomic stabilization outcomes, as well as the beneficial role played by the inflation target as an anchoring device for private inflation expectations. We also demonstrate the potential welfare cost of imperfect public information, and contribute to the debate on optimal monetary policy rule under uncertainty