Short-Run vs. Long-Run Centrality: Production Networks and the Term Structure of Equity
(Job Market Paper)
In this paper, I explore the ability of the term structure of equity to inform
macroeconomic models of production.
I argue that the term structure of equity contains
distinct but complementary information to the term structure of interest rates.
In a simple macroeconomic model of production featuring intersectoral
trade in intermediate goods and investment goods,
I show that because
shocks to intermediate goods hubs play out over shorter horizons
than shocks to investment hubs,
the slope of the
term structure depends crucially on the shape of the production networks and
the covariance structure of sectoral productivity growth.
I introduce the concept of short-run centrality
of industries to characterize this relationship,
testable restrictions between output growth, production networks,
and asset pricing data.
In particular, if the model is to reproduce the stylized
fact that the term structure of equity is downward sloping,
either shocks to investment hubs and intermediate goods hubs must be negatively
or total factor productivity must feature a mean-reverting component.
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.
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...
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.