Neural networks offer a promising tool for the analysis of nonlinear economies. In this paper, we derive conditions for the global stability of nonlinear rational expectations equilibria under neural network learning. We demonstrate the applicability of the conditions in analytical and numerical...

Using a big data set of venture capital financing and related startup firms from Crunchbase, this paper develops a machine-learning model called CapitalVX (for “Capital Venture eXchange”) to predict the outcomes for startups, i.e., whether they will exit successfully through an IPO or...

This paper introduces a reinforcement learning based approach to compute optimal interest rate reaction functions in terms of fulfilling inflation and output gap targets. The method is generally flexible enough to incorporate restrictions like the zero lower bound, nonlinear economy structures...

In this paper we consider regression models with forecast feedback. Agents' expectations are formed via the recursive estimation of the parameters in an auxiliary model. The learning scheme employed by the agents belongs to the class of stochastic approximation algorithms whose gain sequence is...

This paper examines the ordinary least squares (OLS) estimator of the structural parameters in a class of stylised macroeconomic models in which agents are boundedly rational and use an adaptive learning rule to form expectations of the endogenous variable. The popularity of this type of model...

The paper investigates social-learning when the information structure is not commonly known. Individuals repeatedly interact in social-learning settings with distinct information structures. In each round of interaction, they use their experience gained in past rounds to draw inferences from...

Explaining asset price booms poses a difficult question for researchers in macroeconomics: how can large and persistent price growth be explained in the absence large and persistent variation in fundamentals? This paper argues that boom-bust behavior in asset prices can be explained by a model...

This paper models expectation formation by taking into account that agents produce heterogeneous expectations due to model uncertainty, informational frictions and different capacities for processing information. We show that there are two general classes of steady states within this framework:...

In standard models of rational learning from experience, prior uncertainties and disagreements recede smoothly as common evidence accumulates. However, this presumes that the underlying risks are relatively stable. Otherwise, rational learners need to sift random noise for signs that the trend...

Overconfidence has been extensively documented in psychology and economics. This paper studies the long-term interaction between two overconfident agents who learn about common payoff-relevant fundamentals, such as the quality of a joint project or their working environment, and choose how much...