Inflation targeting in a learning economy: an ABM perspective

This paper investigates the performances of an inflation targeting regime in a learning economy framed as an Agent-Based Model (ABM). We keep our ABM as close as possible to the original New Keynesian (NM) model, but we model the individual behaviour of the agents under procedural rationality a la Simon. Accordingly, we assume that their behaviour is guided by simple rules of thumb - or heuristics - while a continuous learning process governs the evolution of those rules. Under these assumptions that also allow the emergence of agents heterogeneity, we analyze the dynamics of the economy without assuming rational expectations, and study the role that a central bank, implementing an inflation targeting regime via a monetary policy rule, can play in the orientation of these dynamics. Consequently, our main goal is to analyse the interplay between the learning mechanisms operating at the individual level and the features and performances of the inflation targeting regime. Our results point to the prime importance of the credibility of central bank's inflation target regarding macroeconomic stabilisation, as well as the beneficial role played by that target as an anchoring device for private inflation expectations. We also establish the potential welfare cost of imperfect public information and contribute to the current debate on optimal monetary policy rules under imperfect common knowledge and uncertainty.