My name is Jeremy Bejarano. I'm a Ph.D. candidate in the Department of Economics at the University of Chicago. My interests are in macroeconomics and finance, with a emphasis on asset pricing.


Sectoral Shifts, Production Networks, and the Term Structure of Equity
(Job Market Paper)
  • 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.

Works in Progress

Asset Pricing and the Importance of Sectoral Shocks
(Draft Coming Soon)
  • 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.
Dividend Growth Dynamics and the Term Structure of Equity
  • I explore the consequences of adding a small, transitory, mean-reverting component to dividend growth dynamics within several classic asset pricing models, such as the consumption CAPM, long-run risk, and external habits. Recent evidence that suggests that the term structure of equity as characterized by holding period returns on dividend strips is downward sloping is at odds with the traditional specification of many of these asset pricing models. I show that these models can have limited success in matching this stylized fact by adjusting cash flow growth dynamics in this way. To understand the principal mechanism, I demonstrate that, within a class of log-linear, affine models, a tight link exists between the risk exposures associated with these holding period returns and the impulse responses of cash flow growth.


Course Material

Here I include material that I developed for courses that I have taught in the past.


  • 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.
  • Jupyter 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.