Séminaire GREDEG : Cees DIKS (University of Amsterdam)

This paper highlights a Bayesian route to randomness in a new class of economic dynamic models, in which boundedly rational agents employ generalized Bayesian learning to update their beliefs regarding unknown model parameters. Under constant gain learning, prior beliefs don't fade out as time proceeds and, as a result, can play a prominent role in shaping the dynamics of the endogenous price fluctuations that emerge. This is illustrated in the context of a cobweb model, for which the effects of behavioral biases and heterogeneity in priors on the endogenous price dynamics can be explicitly investigated. For specific choices of heterogeneous priors and learning rates it is shown that the framework nests a number of well-known models for asset pricing and commodity pricing that have been proposed earlier in the agent-based economic dynamics literature. As such, the proposed framework helps identify and interpret the particular assumptions underlying these models in the broader context of an overarching model.

Bio: 
Cees Diks is Professor of Data Analysis and Economic Statistics at the University of Amsterdam. He holds a master's degree in theoretical physics (Utrecht University) and a PhD in mathematics (Leiden University). Cees is an expert in time series analysis and economic dynamics. In his research at the Center for Nonlinear Dynamics in Economics and Finance (CeNDEF), he develops statistical methods for analysing economic and financial time series and network data, as well as agent-based economic dynamic models in which agents update their expectations based on new information. His current research also includes work on score-based methods for evaluating competing density forecasts and on explainable AI. He acts as the Programme Director for the master programme in Data Science and Business Analytics and is director of the Research Centre for Sustainable Investments and Insurance (RCSII), a joint initiative of the UvA and a.s.r.