Essays in Economic Dynamics with Heterogeneous Expectations

This dissertation discusses various deviations from the hypothesis of Full Information Rational Expectations in modeling economic agents and examines the effects of these deviations on the resulting economic system. Specifically, the work explores how heterogeneous expectations, bounded rationality, and information diffusion impact financial markets and macroeconomic dynamics. Chapter 2 studies a heterogenous asset pricing model in which different classes of investors coexist and evolve, switching among strategies over time according to a fitness measure. In the presence of boundedly rational agents, with biased forecasts and trend following rules, the chapter studies the effect of two types of speculation: one based on fundamentalist and the other on rational expectations. While the first is only based on knowledge of the asset underlying dynamics, the second takes also into account the behavior of other investors. The model is estimated on the Bitcoin Market with two contributions, relying on methods from Machine Learning. First, we construct the Bitcoin Twitter Sentiment Index to proxy a time varying bias. Second, we propose a new method based on a Neural Network, for the estimation of the resulting heterogeneous agent model with rational speculators. We show that the switching finds support in the data and that while fundamentalist speculation amplifies volatility, rational speculation has a stabilizing effect on the market. Chapter 3 investigates the interplay between information diffusion in social networks and its impact on financial markets using an agent-based model. Agents receive and exchange information about an observable stochastic component of the dividend process of a risky asset. A small proportion of the network has access to a private signal about the component, which can be either clean (information) or distorted (misinformation). Other agents are uninformed and can receive information only from their peers. All agents update their beliefs in a Bayesian manner, but they do so in a behavioral way, where they replace true precision with an individual parameter that depends on an endogenous and time-evolving measure of the agent's confidence in the source of the information. We examine, through simulations, how information diffuses in the network and provide a framework to account for the delayed absorption of shocks that are not immediately priced, as predicted by classical financial models. We show the effect of network topology on the resulting asset price and offer an interpretation for excess volatility relative to fundamentals, persistence amplification, and leptokurtosis of returns. Chapter 4 analyzes a macroeconomic model in which agents have sticky expectations. On the empirical side, we provide evidence of a departure from rationality in household expectations collected from survey data. Moreover, we show that this departure results in sticky expectations that are heterogeneous across agents. This heterogeneity is driven by wealth differences. This finding is incorporated into a Heterogeneous Agents New Keynesian Model to revisit monetary policy. Our quantitative analysis shows that we are able to match the empirical evidence of the hump-shaped response of inflation to a monetary policy shock with micro-evidence of households' expectations from survey data.