- In this paper, I argue that the term structure of equity as characterized by expected holding period returns on dividend strips can be used as a diagnostic to evaluate the quantity dynamics that arise in a macroeconomic model. I do this by showing that the risk exposures associated with dividend futures are equal to the impulse responses aggregate consumption with respect to the underlying shocks. As an application, I derive the asset pricing implications of a multi-sector production network model and use this to shed light on relative importance of idiosyncratic and aggregate total factor productivity (TFP) shocks. Though aggregate TFP in the U.S. over the last 60 years has grown approximately 1.4 percent annually, these gains have been dispersed across individual sectors, with some sectors even seeing substantial declines. This dispersion is either the result of idiosyncratic sectoral shocks or aggregate shocks that shift the composition of the economy without necessarily affecting long-run aggregate output. Decomposing the contribution of each shock to this term structure of equity, I show that the shift shocks contribute to a downward sloping term structure of equity while others contribute to an upward sloping term structure. Thus, imposing a downward sloping term structure in this model amounts to putting a lower bound on the contribution of aggregate shifts relative to other shocks.

- In this paper, I propose using risk prices inferred from asset returns data to measure the relative importance of sectoral TFP shocks. Risk prices measure the marginal compensation that a representative investor requires in exchange for a unit increase in exposure to a source of macroeconomic risk. I utilize the shock-price elasticities developed in Borovička and Hansen (2014) to characterize these risk prices in a set of multisector models. I show that in a simple two-period model production network model, the measure of relative importance a sector's shocks is the same whether we use Domar weights, the network-based influence vector measure of Acemoglu et al (2012), or the shock's associated risk price. In contrast, I show that these measures can differ in multi-period models. I analyze several such models. Using the TFP shocks identified by each model, I propose measuring these risk prices empirically by projecting the sectoral shock onto a panel of asset returns to construct factor mimicking portfolios and measuring the associated returns and factor premia.

- ECON 21410: Computational Methods in Economics (Spring 2019)

This course introduces the basic programming and computational techniques necessary for solving and estimating economic models. The course covers topics in numerical methods, such as optimization, function approximation, and Monte Carlo techniques, as well as topics in data exploration... (Click here for course evaluations) - ECON 21410: Computational Methods in Economics (Spring 2018)
- Parallel Programming with Python and MPI: BYU 2013

This is the material, including a short text I wrote and video tutorials, for a workshop on parallel programming with MPI and Python that I taught as part of a summer bootcamp on computational economics in 2013. It may be quite out of date by now.

- Interactive Plot and Widget Demo. Here I present examples of the kinds of interactive plots and widgets that can be easily embedded into a website using tools that work well with or are based in the Python/R ecosystem.
- Jupter Notebook: Fixed and Random Effects Models in Python, R, and Stata . This should be updated, but some have found this useful. It's a Jupyter notebook in which I replicate some examples from Wooldridge's panel data book. It provides side-by-side code showing how to implement fixed and random effects models in Python (using the statsmodels and linearmodels packages), R, and Stata.