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
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
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
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.
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.
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.