Economic analysis of effective policies for managing epidemics
requires an integrated economic and epidemiological approach. We
develop and estimate a spatial, micro-founded model of the joint
evolution of economic variables and the spread of an epidemic.
We empirically discipline the model using new U.S. county-level
data on health, mobility, employment outcomes, and
non-pharmaceutical interventions (NPIs) at a daily frequency.
Absent policy or medical interventions, the model predicts an
initial period of exponential growth in new cases, followed by a
protracted period of roughly constant case levels and reduced
economic activity. Nevertheless, if vaccine development proved
impossible, and suppression cannot entirely eradicate the
disease, a utilitarian policymaker cannot improve significantly
over the laissez-faire equilibrium by using lockdowns.
Conversely, if a vaccine will arrive within two years, NPIs can
improve upon the laissez-faire outcome by dramatically
decreasing the number of infectious agents and keeping
infections low until vaccine arrival. Mitigation measures that
reduce viral transmission (e.g., mask-wearing) both reduce the
virus's spread and increase economic activity.

A Class of Time-Varying Parameter Structural VARs for Inference
under Exact or Set Identification

This paper develops a new class of structural vector
autoregressions (SVARs) with time-varying parameters, which I
call a drifting SVAR (DSVAR). The DSVAR is the first structural
time-varying parameter model to allow for internally consistent
Bayesian inference under exact--or set--identification, nesting
the widely used SVAR framework as a special case. I prove that
the DSVAR implies a reduced-form representation, from which
structural inference can proceed similarly to the widely used
two-step approach for SVARs: first estimating reduced-form
parameters and then imposing identifying restrictions to choose
among the set of observationally equivalent structural
parameters consistent with the reduced-form estimates. In a
special case, the reduced form implied by the DSVAR is a
tractable known model for which I provide the first algorithm
for Bayesian estimation of all free parameters. I demonstrate
the framework in the context of Baumeister and Peersman's (2013)
work on time variation in the elasticity of oil demand.

We develop a sequential Monte Carlo (SMC) algorithm for Bayesian
inference in vector autoregressions with stochastic volatility
(VAR-SV). The algorithm builds particle approximations to the
sequence of the model's posteriors, adapting the particles from
one approximation to the next as the window of available data
expands. The parallelizability of the algorithm's computations
allows the adaptations to occur rapidly. Our particular
algorithm exploits the ability to marginalize many parameters
from the posterior analytically and embeds a known Markov chain
Monte Carlo (MCMC) algorithm for the model as an effective
mutation kernel for fighting particle degeneracy. We show that,
relative to using MCMC alone, our algorithm increases the
precision of inference while reducing computing time by an order
of magnitude when estimating a medium-scale VAR-SV model.

Vector autoregressions with Markov-switching parameters
(MS-VARs) offer substantial gains in data fit over VARs with
constant parameters. However, Bayesian inference for MS-VARs has
remained challenging, impeding their uptake for empirical
applications. We show that Sequential Monte Carlo (SMC)
estimators can accurately estimate MS-VAR posteriors. Relative
to multi-step, model-specific MCMC routines, SMC has the
advantages of generality, parallelizability, and freedom from
reliance on particular analytical relationships between prior
and likelihood. We use SMC’s flexibility to demonstrate that
model selection among MS-VARs can be highly sensitive to the
choice of prior.

Fully Bayesian Inference for Large Vector Autoregressions (with
Stochastic Volatility)

Draft coming soon.

Measuring The Effect of Health Insurance on Consumer
Bankruptcies from the ACA Medicaid Expansion

with Daniel Kolliner and
Kurt Mitman
Draft coming soon.

Resting Papers

An Empirical Analysis of Time-Varying Fiscal Multipliers

Mimeo, University of Pennsylvania. [Draft][Abstract]

Debates among policy makers about the appropriate response of
fiscal policy to the Great Recession centered on the size of the
fiscal multiplier, defined as the number of dollars that output
increases in response to a dollar of fiscal stimulus. Mostly
using the structure of micro-founded Dynamic Stochastic General
Equilibrium models,macroeconomists have argued that fiscal
multipliers may vary over time and be particularly large in
liquidity traps or during recessions. I extend existing
techniques for the Bayesian estimation of vector autoregressions
with Markov-switching in selected coefficients to empirically
investigate both the extent of time-variation in fiscal
multipliers and what factors cause the variation. In
contradiction to recent results in the literature, my estimates
suggest that the value of the government spending multiplier is
likely smaller in recessions than in expansions, while tax cuts
have a greater effect in recessions than in expansions. I find
little evidence that regime change in monetary policy rules and
fiscal policy rules have caused time variation in the value of
the fiscal multiplier.

Federal Reserve Articles

A Forecasting Assessment of Market-Based PCE Inflation Economic Commentary, 2020-01.
[HTML][PDF]Has the Real-Time Reliability of Monthly Indicators Changed
over Time? Economic Commentary, 2019-16.
[HTML][PDF]An Assessment of the ISM Manufacturing Price Index for
Inflation Forecasting,
with Tristan Young
Economic Commentary, 2018-05.
[HTML][PDF]New Normal or Real-Time Noise? Revisiting the Recent Data on
Labor Productivity,
with John Zito
Economic Commentary, 2016-16.
[HTML][PDF]Does GDI Data Change our Understanding of the Business
Cycle?with
Christian Garciga Economic Trends, 01.14.16.
[HTML][PDF]US Fiscal Policy: Recent Trends in Historical Context,
with Sara Millington
Economic Trends, 07.14.15.
[HTML][PDF]