New Estimates of US Markups and Their Reactions to Aggregate Demand
Joint with Jens
Hilscher, and Tengda Gong
July, 2025
(subsumes "Procyclical Markups: Nonparametric Estimates in a Structural Dynamic Panel") SSRN version
This paper proposes new estimates of US markups and investigates the contributions
of aggregate demand and market concentration measures to their dynamics. We proceed
in two steps. First, we show that markups can be nonparametrically estimated based on
a locally-linear demand model. We apply a revenue-weighted instrumental variable approach
to estimate region-specific time-varying markups using US grocery price scanner data from 2001-2022.
We find a secular trend rise and unconditional procyclical variation in markups. Second, using a dynamic panel model,
we quantify the contributions of four main drivers of conditional variation: demographic trends, market concentration,
aggregate demand, and the real interest rate. Taken together, these factors explain a large fraction of markup variation across markets and over time.
The variations in demographic and market concentration factors had only small impacts, which moved in opposite directions.
From 2008-2016 negative real interest rates increased markups counteracting downward pressure
from aggregate demand factors; later in the sample markups increased because
of higher aggregate demand. Over our sample period, both overall and food-at-home
CPI inflation were similar to markup growth, suggesting that real marginal costs may have declined.
Wild inference for wild SVARs with application to heteroscedasticity-based IV
Structural vector autoregressions are used to compute impulse response functions
(IRF) for persistent data. Existing multiple-parameter inference requires cumbersome
pretesting for unit roots, cointegration, and trends with subsequent stationarization.
To avoid pretesting, we propose a novel dependent wild bootstrap procedure for simul-
taneous inference on IRF using local projections (LP) estimated in levels in possibly
nonstationary and heteroscedastic SVARs. The bootstrap also allows efficient smooth-
ing of LP estimates.
We study IRF to US monetary policy identified using FOMC meetings count as an
instrument for heteroscedasticity of monetary shocks. We validate our method using
DSGE model simulations and alternative SVAR methods.
It has become standard for empirical studies to conduct inference robust to cluster dependence
and heterogeneity. With a small number of clusters, the normal approximation for the
t-statistics of regression coefficients may be poor. This paper tackles this problem using a
critical value based on the conditional Cramér-Edgeworth expansion for the t-statistics.
Our approach guarantees third-order refinement, regardless of whether a regressor is discrete or
not, and, unlike the cluster pairs bootstrap, avoids resampling data. Simulations show that our
proposal can make a difference in size control with as few as 10 clusters.
Price Dynamics of Organic versus Conventional Fresh Produce
Fresh produce is the largest category of organic food sales in the U.S. This paper studies the
relative price dynamics of organic versus conventional produce from 2006 to 2023. Using retail
scanner data, we find that the revenue-weighted average price of organic produce declined
relative to conventional produce across major grocery markets beginning in 2017, with the
exception of a brief uptick following the onset of COVID-19. Prior to 2017, the relative price
exhibited substantial fluctuations without a clear trend. Our analyses show that macroeconomic
and market factors--real interest rate, housing prices, unemployment, product supply and variety
as well as retailer concentration--account for a substantial share of this variation, especially
the post-2016 downward trend. In addition, weather conditions in major fruit- and
vegetable-producing states--precipitation and temperature--contribute significantly to
short-term price fluctuations and place upward pressure on the relative price.
Diesel Price Shocks and Food Inflation: Unequal Pass-Through to Milk Prices
This paper investigates the contribution of diesel price shocks to food inflation using the
example of milk in California. We estimate the dynamic responses of milk prices to an initial
shock in diesel prices over the following year using panel local projection. Our estimates are
for the overall average effect of diesel price shocks, given supply chain conditions in
California, e.g., production, transport, processing, and contractual arrangements. We find strong
evidence of a delayed response of milk prices to diesel price shocks. The total effect peaks at
four to seven months and is equal to 20%. Importantly, the effect of diesel prices is unequal
across different types of milk. Private label (store brand) milk--the most popular and
lowest-price milk--has the highest pass-through. In contrast, the pass-through of higher-priced
national brand organic milk is close to zero. The diesel price surge of 2022 affected milk
prices unequally. More affordable varieties saw significantly higher effects of diesel price
shocks compared to premium options.
Identification in dynamic models using sign restrictions
Sign restrictions on impulse response functions are used in the literature to identify
structural vector autoregressions and structural factor models. I extend the rank condition used
for exclusion restrictions and provide a necessary and sufficient conditions for point
identification for sign restrictions in this class of models. The necessary condition for point
identification implies that as the number of sign restrictions grows a subset with sufficient
number of sign restrictions becomes binding in the limit. However, one does not need to possess
information about this subset to achieve point identification. So when exclusion restrictions
are not justified by theory, sign restrictions can provide an alternative way to get
point-identified impulse response functions. Also further, I present a closed form
representation of the set of all impulse response functions satisfying a set of sign
restrictions. I demonstrate that restrictions on responses to all shocks can dramatically shrink
this set when compared to restrictions only on a small number of shocks.
2013 23rd Annual Meeting of the Midwest Econometrics Group, Indiana University,
Bloomington
We study the properties of the classical projection method to conduct simultaneous inference
about the coefficients of the structural impulse-response function and their identified set in
Structural Vector Autoregressions. We show that -- as the sample size grows large --
projection inference produces regions for the structural parameters and their identified set
with both frequentist coverage and robust Bayesian credibility of at least 1-alpha.
We then calibrate the radius of the Wald ellipsoid to guarantee that -- for a given posterior on
the reduced-form parameters -- the robust Bayesian credibility of the projection method is
exactly 1-alpha. We illustrate the main results of the paper using a demand/supply model of the
U.S. labor market.
2015 11th World Congress Econometric Society, Montreal, Canada
2015 22nd International Symposium on Mathematical Programming, Pittsburgh, USA
2015 PSU-Cornell Macro Workshop, Pennsylvania State University, State College, USA
2015 Annual Conference of the Royal Economic Society, University of Manchester, UK
2015 Higher School of Economics, Moscow, Russia
2014 Latin American Meeting of The Econometric Society, University of São Paulo, Brazil
Price sensitivity to precipitation and water storage in California
Joint with M. Turland , J. Hilscher, C. Carter, and K. Jessoe,
Nature Sustainability, volume 8, pages 1505–1512 (2025)
Climate change will reshape global demand and supply for water. Surface water supplies will
become more variable, and warming temperatures may increase demand for water. These changes
could impose substantial economic costs. Well-functioning water markets can mitigate some of
these costs by allocating water to those who value it most. However, storage constraints limit
the ability of markets to transfer water over time. Here we use transactions data from 2010 to
2022 to evaluate how water prices in California’s surface and groundwater markets respond to
precipitation shocks, and how this response varies with inventory levels and water storage
capacity. In groundwater markets, with high inventories, prices are unresponsive to
precipitation shocks. In surface water markets, with limited inventories, prices increase
strongly when precipitation declines. A 50-inch decrease in annual precipitation, typical when
comparing deluge with drought in California, increases the price by US$487 per acre-foot, more
than tripling compared with the average wet year. This effect is less pronounced when inventory
levels are higher. Increasing storage through the joint management of groundwater and surface
water supplies could provide a pathway to reduce the adverse consequences of climate-induced
precipitation volatility.
Bias correction for quantile regression estimators
We study the bias of classical quantile regression and instrumental variable quantile regression estimators.
While being asymptotically first-order unbiased, these estimators can have non-negligible second-order biases.
We derive a higher-order stochastic expansion of these estimators using empirical process theory.
Based on this expansion, we derive an explicit formula for the second-order bias and propose a feasible bias correction procedure that uses finite-difference estimators of the bias components.
The proposed bias correction method performs well in simulations. We provide an empirical illustration using Engel’s classical data on household food expenditure.
California Gasoline Demand Elasticity Estimated Using Refinery Outages
This paper presents new gasoline demand price elasticity estimates for California. We use
unique characteristics of California's gasoline market and a new set of proposed instruments.
As a first step, we take advantage of California's unique gasoline market, which is partially
isolated from the rest of the United States due to environmental regulations. We control for
persistent demand shocks and estimate a lower bound for the long-run elasticity of demand of
-0.23.
In the second step, we use a new set of instruments to control for simultaneity. We use
detailed information on refinery outages to capture short-run supply shocks. Our estimate of
long-run demand elasticity is -0.60.
Simple subvector inference on sharp identified set in affine models
Journal of Econometrics, 2025, Vol. 249 (subsumes
"Inference in high-dimensional set-identified linear models") Arxiv version
This paper studies a regularized support function estimator for bounds on components of the
parameter vector in the case in which the identified set is a polygon. The proposed regularized
estimator has three important properties: (i) it has a uniform asymptotic Gaussian limit in the
presence of flat faces in the absence of redundant (or overidentifying) constraints (or vice
versa); (ii) the bias from regularization does not enter the first-order limiting
distribution; (iii) the estimator remains consistent for sharp (non-enlarged) identified set
for the individual components even in the non-regular case.
These properties are used to construct uniformly valid confidence sets for an element theta_1 of
a parameter vector theta in R^d that is partially identified by affine moment equality and
inequality conditions. The proposed confidence sets can be computed as a solution to a small
number of linear and convex quadratic programs, leading to a substantial decrease in
computation time and guarantees a global optimum.
On model selection criteria for climate change impact studies
Climate change impact studies inform policymakers on the estimated damages of future climate
change on economic, health and other outcomes. In most studies, an annual outcome variable is
observed, e.g. agricultural yield, along with a higher-frequency regressor, e.g. daily
temperature. Applied researchers then face a problem of selecting a model to characterize the
nonlinear relationship between the outcome and the high-frequency regressor to make a policy
recommendation based on the model-implied damage function. We show that existing model selection
criteria are only suitable for the policy objective if one of the models under consideration
nests the true model. If all models are seen as imperfect approximations to the true nonlinear
relationship, the model that performs well in the normal climate conditions is not guaranteed to
perform well at the projected climate that is different from the historical norm. We therefore
propose a new criterion, the proximity-weighted mean-squared error (PWMSE), that directly
targets precision of the damage function at the projected future climate. To make this criterion
feasible, we assign higher weights to prior years that can serve as weather analogs to the
projected future climate when evaluating competing models using the PWMSE. We show that our
approach selects the best approximate regression model that has the smallest weighted error of
predicted impacts for a projected future climate. A simulation study and an application
revisiting the impact of climate change on agricultural production illustrate the empirical
relevance of our theoretical analysis.
Time Consistency and Duration of Government Debt: A Model of Quantitative Easing∗
All finite single-agent choice problem with ordinal preferences admit a compatible utility
function such that: strict dominance by pure or mixed actions coincides with dominance by pure
actions in the sense of Börgers (1993). With asymmetric preferences, Börgers’ notion of
dominance reduces to the classical notion of strict dominance by pure strategies. The result
extends to some infinite environments satisfying different assumptions. In all cases, the
equivalence holds whenever the agent is sufficiently risk averse.
Phillips curve and development of the labor market in Russia,
The Economic Journal of the Higher School of Economics (in Russian), 2011, Volume 15, Issue 2, 155--176 Open PDF
Other publications
Can Imports Stabilize California Gasoline Prices in the Face of Refinery Closures?
