In this paper, we analyze the process of money creation in a credit economy. We start from the consideration that the traditional money multiplier is a poor description of this process and present an alternative and dynamic approach that takes into account the heterogeneity of agents in the...

In this paper, I propose a novel way to model sentiments in asset prices. Under this new representation, sentiments, or animal spirits, are sparked by exogenous shocks to beliefs, but feed on the uncertainty generated by imperfect information. Sentiments cause expectations to deviate from...

In a model of incomplete, heterogeneous information, with externalities and strategic interactions, we analyze the possibility for learning to act as coordination device. We build on the framework proposed by Angeletos and Pavan (2007) and extend it to a dynamic multiperiod setting where agents...

This paper proposes a novel interpretation of the constant gain learning algorithm through a probabilistic setting with Bayesian updating. Such framework allows to understand the gain coefficient in terms of the probability of changes in the estimated quantity.

This work analyses the role of asymmetry in beliefs for price dynamics in a cobweb model with heterogeneous expectations and evolutionary selection of predictors. While heterogeneous but symmetric beliefs result in the rational expectations equilibrium price, the effect of asymmetry depends on...

Why are stock prices much more volatile than the underlying dividends? The excess volatility of prices can in principle be attributed to two different causes: time-varying discount rates for expected future dividends, arising from variation in risk premia; or the irrational exuberance of...

We compare forecasts from different adaptive learning algorithms and calibrations applied to US real-time data on inflation and growth. We find that the Least Squares with constant gains adjusted to match (past) survey forecasts provides the best overall performance both in terms of forecasting...

Adaptive learning introduces persistence in the evolution of agents' beliefs over time. For applied purposes this is a convenient feature to help explain why economies present sluggish adjustments towards equilibrium. The pace of learning is directly determined by the gain parameter, which...

Under adaptive learning, recursive algorithms are proposed to represent how agents update their beliefs over time. For applied purposes these algorithms require initial estimates of agents perceived law of motion. Obtaining appropriate initial estimates can become prohibitive within the usual...

We review and evaluate methods previously adopted in the applied literature of adaptive learning in order to initialize agents' beliefs. Previous methods are classified into three broad classes: equilibrium-related, training sample-based, and estimation-based. We conduct several simulations...