We study the possibility of (almost) self-fulfilling waves of pessimism and selfreinforcing liquidity traps in a New Keynesian model with heterogeneous expectations. We explicitly focus on the "anchoring" of expectations that is modeled as the range of deviations from the central bank targets...

We analyze fiscal consolidations using a New Keynesian model where agents have heterogeneous expectations and are uncertain about the composition of consolidations. We look at spending-based and tax-based consolidations and analyze their effects separately. We find that the effects of...

This essay links some of my own work on expectations, learning and bounded rationality to the inspiring ideas of Jean-Michel Grandmont. In particular, my work on consistent expectations and behavioral learning equilibria may be seen as formalizations of JMG's ideas of self-fulfilling mistakes....

We analyze fiscal consolidations using a New Keynesian model where agents have heterogeneous expectations and are uncertain about the composition of consolidations. We look at spending-based and tax-based consolidations and analyze their effects separately. We find that the effects of...

We introduce Behavioral Learning Equilibria (BLE) into a multivariate linear framework and apply it to New Keynesian DSGE models. In a BLE, boundedly rational agents use simple, but optimal AR(1) forecasting rules whose parameters are consistent with the observed sample mean and autocorrelation...

We introduce the concept of behavioral learning equilibrium (BLE) into a high dimensional linear framework and apply it to the standard New Keynesian model. For each endogenous variable, boundedly rational agents use a simple, but optimal AR(1) forecasting rule with parameters consistent with...

We introduce behavioral learning equilibria (BLE) into a multi-variate linear framework and apply it to New Keynesian DSGE models. In a BLE, boundedly rational agents use simple but optimal first-order autoregressive (AR(1)) forecasting rules whose parameters are consistent with the observed...