Behavioral Learning Equilibria, Persistence Amplification & Monetary Policy

We generalize the concept of behavioral learning equilibrium (BLE) to a general high dimensional linear system and apply it to the standard New Keynesian model. Boundedly rational agents learn to use a simple AR(1) forecasting rule for each variable with parameters consistent with the observed sample mean and autocorrelation of past data. Agents do not fully recognize the more complex structure of the economy, but learn to use an optimal simple AR(1) rule. We find that BLE exists, under general stationarity conditions, typically with near unit root autocorrelation parameters. BLE thus exhibits a novel feature, persistence amplification: the persistence in inflation and output gap is much higher than the persistence in exogenous fundamental driving factors. In a boundedly rational world, coordination of individual expectations on an aggregate outcome described by our simple, parsimonious BLE seems more likely. We also consider monetary policy under BLE for different Taylor interest rate rules and study whether inflation and/or ouput gap targeting can stabilize coordination on near unit root BLE.