This notebook illustrates how to fit aggregate random coefficient logit models in Stan, using Bayesian techniques. It’s far easier to learn and implement than the standard BLP algorithm, and has the benefits of being robust to mismeasurement of market shares, and giving limited-sample posterior uncertainty of all parameters (and demand shocks). This comes at the cost of modeling firms’ price-setting process, including how unobserved product-market demand shocks affect prices.

Code and data available at github.com/stan-dev/stancon_talks