Zach Brown

Zach Y. Brown

I am a post-doctoral fellow at the National Bureau of Economic Research in Cambridge, MA. In Fall 2018, I will be an Assistant Professor of Economics at University of Michigan.

I am interested in industrial organization, applied microeconomics, and health economics. My current research examines issues related to information frictions and competition. I received my PhD in economics from Columbia University in 2017.

Phone: (617) 588-1464




Stan is a new open-source probabilistic programming language well suited for econometrics. Compared to standard statistical programming languages, Stan is flexible (easy to switch between MLE and MCMC) and fast (utilizes automatic differentiation to compute gradients). Bayesian methods using Stan are particularly useful when large datasets or high dimensional unobservables make standard methods infeasible.

Basic examples that can be easily modified are below:
Stan is well suited for estimating demand models where some consumers lack full information about product characteristics (see research for more information). A simple version of the model is below: Code requires Matlab, MatlabStan, and CmdStan. For information about using Stan for time-series models in economics see here.