Modeling the Evolution of Expectations and Uncertainty in General Equilibrium

Abstract

We develop methods to solve general equilibrium models in which forward-looking agents are subject to waves of pessimism, optimism, and uncertainty that turn out to critically a¤ect macroeconomic outcomes. Agents in the model are fully rational, conduct Bayesian learning, and they know that they do not know. Therefore, agents take into account that their beliefs will evolve according to what they will observe. This framework accommodates both gradual and abrupt changes in beliefs and allows for an analytical characterization of uncertainty. We use a prototypical Real Business Cycle model to illustrate the methods.JEL Codes: D83, C11, E22.

Monetary policy uncertainty

“…10 9 For details of our sampling technique, please see our audit guide available at: https://sites.google.com/site/bosun09/monetary-policy-uncertainty-index. 10 In our pilot human audit, we noticed for instance that articles in the 1980s and early 1990s use "discount rate" to refer to the monetary policy instrument, while such reference diminished completely in recent years. In addition, we see some articles using words such as "anxiety" and "fear" to discuss uncertainty related to monetary policy.…”

Informal one‐sided target zone model and the Swiss franc

“… The modeling toolbox developed here may be combined with the framework developed by Bianchi and Melosi () to characterize the evolution of expectations and uncertainty in general equilibrium models in which agents have to learn the stochastic properties of regimes. The central insight of Bianchi and Melosi () consists of recognizing that the evolution of agents' beliefs can be captured by defining a set of regimes that are characterized by the degree of agents' pessimism, optimism, and uncertainty about future equilibrium outcomes.…”

Term Structure of Uncertainty in the Macroeconomy

“…For Markov chain models used to capture the regime shift dynamics of exogenous shocks see David (2008), Chen (2010) or for some recent examples in the asset pricing literature and Liu et al (2011) and in macroeconomic modeling. Regime switches are also utilized to model time-variation in government policies, see Sims and Zha (2006), Liu et al (2009) and Bianchi (2012) for regime switching in monetary policy rules, Davig et al (2010Davig et al ( , 2011 and Bianchi and Melosi (2015) for fiscal policy applications and Chung et al (2007) and Bianchi and Ilut (2015) for a combination of both. Farmer et al (2011) andFoerster et al (2014) analyze solution and estimation techniques in Markov chain models in conjunction with perturbation approximation methods.…”