Online workshop on Structural Bayesian techniques for economic experiments

[james...@gmail.com]

Dear all,

On Thursday October 16th I will be presenting an online workshop based on material from my book "Structural Bayesian Techniques for Experimental and Behavioral Economics, with applications in R and Stan" (https://jamesblandecon.github.io/StructuralBayesianTechniques/section.html) as part of the "Workshops for Ukraine" series. Details of how to sign up can be found here: https://sites.google.com/view/dariia-mykhailyshyna/main/r-workshops-for-ukraine#h.cbn6waob96u

Here is a description of what you can expect: 

If you have an economic model, then in principle you can estimate its parameters using structural techniques. In experimental and behavioral economics these models typically describe how people make decisions, describing phenomena such as distributional preferences, risk-aversion, time preferences, levels of strategic thinking, and strategies used by players in games. Bayesian techniques lend themselves especially well to estimating these models for at least three reasons. Firstly, hierarchical Bayesian modeling handles well the heterogeneity we can expect there to be between participants in economic experiments. Secondly, when doing structural estimation, we are often interested in reporting transformations of our model's parameters, and this is very easy to implement in the Bayesian framework. Finally, recent developments in the software for estimating Bayesian models have made doing this easier and less computationally taxing. 

This workshop will introduce you to structural modelling of data from economic experiments. We will use data from real economic experiments to estimate these models using Stan. Stan is a free probabilistic programming language, and is usable within R with the library RStan. Using a model of other-regarding preferences as an example, we will learn about how to build a full probabilistic model from an economic model that makes deterministic predictions. We will then build into this model some plausible between-participant heterogeneity. Finally, we will use our model to make an out-of-sample prediction.

If you have any questions, please contact me at james...@utoledo.edu

All the best,

James Bland