This paper examines the cognitive biases in small and medium-sized enterprise (SME) managerial beliefs under exogenous shocks and investigates whether such biases affect firms’ decision-making in the future. We use unique administrative survey data spanning the outbreak of COVID-19 in China...

This paper examines key facts about the U.S. housing market. The price to rent ratio is highly volatile and significantly autocorrelated. Returns on housing are positively autocorrelated. The price to rent ratio is negatively correlated with future returns on housing and future rent growth....

This paper studies the role of observational learning in search markets where buyers do not take the list price as a take-it-or-leave-it offer. Using a unique data from the Beijing housing market, we estimate a structural model in which buyers infer a seller’s reservation value from the...

Recent evidence suggests that lifetime experiences play an important role in determining households' investment choices. I incorporate these findings and the fact that household portfolios are underdiversified into an otherwise standard life-cycle model and examine to what extent they can help...

In this paper, we create machine learning (ML) models to forecast home equity credit risk for individuals using a real-world dataset and demonstrate methods to explain the output of these ML models to make them more accessible to the end-user. We analyze the explainability of these models for...

I study the effects of improved public information on equilibrium welfare and price dispersion, providing sufficient conditions for negative and positive effects. Public information affects welfare by reducing excessive (though rational) pessimism induced by sequential learning. Reduced...

We study the dynamics of a Lucas-tree model with finitely lived agents who "learn from experience." Individuals update expectations by Bayesian learning based on observations from their own lifetimes. In this model, the stock price exhibits stochastic boom-and-bust fluctuations around the...

We study the properties of generalized stochastic gradient (GSG) learning in forward-looking models. We examine how the conditions for stability of standard stochastic gradient (SG) learning both differ from and are related to E-stability, which governs stability under least squares learning. SG...

In this article, a modelling framework for the information transmission between agents in an evolutionary game setting is proposed. Agents observe traits which reaect past and present behaviour and success of other agents. If agents imitate more successful agents based on these traits, the...

Models with heterogeneous interacting agents explain macro phenomena through interactions at the micro level. We propose genetic algorithms as a model for individual expectations to explain aggregate market phenomena. The model explains all stylized facts observed in aggregate price fluctuations...