Some studies have expressed concern that the Gaussian-inverse Wishart-Haar prior typically employed in estimating sign-identified VAR models may be unintentionally informative about the prior for the structural responses. We discuss what features to look for in this prior in the absence of specific prior information about the responses, building on the notion of weakly informative priors in Gelman et al. (2013), and in the presence of such information. Empirical examples illustrate that the Gaussian-inverse Wishart-Haar prior need not be unintentionally informative. Even when it is, there are empirically verifiable conditions under which this fact becomes immaterial for the substantive conclusions.

This paper documents the effect of the oil embargo and price cap on Russian oil prices and oil exports in the wake of the Russian invasion of Ukraine in February 2022. We show that the embargo forced Russia to accept a $32/bbl discount on its Urals export price in March 2023 relative to January 2022, nearly half of which is directly attributable to the higher cost of shipping crude oil over longer distances, as Russia diverted most of these exports to India. Based on a calibrated model of global oil supply and demand, the remainder ($17/bbl) can be explained by increased Indian bargaining power in the Urals market. We also provide a similar analysis for the ESPO price discount on exports to China. The price cap, in contrast, may have alleviated concerns about a possible disruption of Russian oil supplies from a broader ban on the use of Western maritime services, but its effect on Russian oil export prices in March 2023 was negligible.JEL Classification: F51, International Conflicts, Negotiations, Sanctions, Q41, Energy: Demand and Supply, Prices, Q48, Energy: Government Policy

A common VAR approach is to identify responses to TFP news shocks by maximizing the variance share of TFP over a long horizon. We find that these TFP max share estimators tend to be biased in large samples when applied to data generated from DSGE models with shock processes that match TFP moments in the data, especially in the presence of TFP measurement error. We propose an alternative max share news estimator that reduces this bias and the RMSE of the impulse response estimates, even when there is sizable measurement error in the news variable. When applying this estimator to U.S. data, we find that news shocks are slower to diffuse to TFP and have a smaller effect on real activity than implied by the TFP max share estimator.

We propose a semiparametric local projection estimator of nonlinear impulse response functions for a broad class of structural dynamic models relevant for applied macroeconomics, including models with nonlinearly transformed regressors, state dependent coefficients, and nonlinear interactions between shocks and state variables. The estimator is based on a doubly robust moment condition that identifies the average response function as a linear functional of a nonparametric conditional mean, augmented by a density ratio that captures the effect of shifting the shock of interest. We combine this moment condition with cross-fitting that handles serial dependence. The resulting estimator is √(T)-consistent and asymptotically normal. We examine the finite-sample performance of the estimator across a range of nonlinear data generating processes and illustrate its use in two empirical examples.

We provide evidence that the quantitative importance of the Haar prior for posterior impulse response inference has been overstated. How sensitive posterior inference is to the Haar prior depends on the width of the identified set. This width depends not only on how much the identified set is narrowed by the identifying restrictions but also on the data through the reduced-form model parameters. Hence, the role of the Haar prior can be assessed only on a case-by-case basis. We show by example that when the identification is sufficiently tight, posterior inference based on a Gaussian-inverse Wishart-Haar prior is justified.

A common practice in empirical macroeconomics is to examine alternative recursive orderings of the variables in structural vector autoregressive (VAR) models. When the implied impulse responses look similar, the estimates are considered trustworthy. When they do not, the estimates are used to bound the true response without directly addressing the identification challenge. A leading example of this practice is the literature on the effects of uncertainty shocks on economic activity. We prove by counterexample and show by simulation that this practice is invalid, whether the data generating process is a structural VAR model or a dynamic stochastic general equilibrium model. Simulation evidence suggests that the underlying identification challenge can be addressed using an instrumental variables estimator.

Structural impulse response functions may be estimated based on priors about the parameters of the structural VAR presentation. Even when such priors appear seemingly reasonable, they may imply an unintentionally informative prior for the structural impulse responses. Rather than pretending that the posterior of the impulse responses does not depend on this prior, the proposal in this paper is to verify that the prior distribution of the vector of impulse responses of interest is not unintentionally informative. Moreover, if the impulse response prior is intentionally informative, this point must be conveyed, so the reader can properly evaluate the reported conclusions. This paper discusses easy-to-use diagnostic tools that help practitioners address these concerns.

This Economic Letter examines the historical relationship between oil price shocks and inflation in light of some recent research and goes on to discuss what the recent jump in oil prices might mean for inflation in the future.

It is common in applied work to estimate responses of macroeconomic aggregates to news shocks derived from surprise changes in daily futures prices around the date of policy announcements. This requires mapping the daily surprises into a monthly shock that may be used as an external instrument in a monthly VAR model or local projection. The standard approach has been to sum these daily surprises over the course of a given month when constructing the monthly proxy variable, ignoring the accounting relationship between daily and average monthly price data. In this paper, I discuss an alternative approach to constructing monthly proxies from daily surprises that takes account of this link and revisit the question of how to use OPEC announcements to identify news shocks in VAR models of the global oil market. The proposed approach calls into question the interpretation of the identified shock as oil supply news and implies quantitatively and qualitatively different estimates of the macroeconomic impact of OPEC announcements.

We propose a new instrument for estimating the price elasticity of gasoline demand that exploits systematic differences across U.S. states in the pass-through of oil price shocks to retail gasoline prices. We show that these differences are primarily driven by the cost of producing and distributing gasoline, which varies with states’ access to oil and gasoline transportation infrastructure, refinery technology, and environmental regulations, creating cross-sectional gasoline price shocks in response to an aggregate oil price shock. Time-varying estimates do not support the view that the gasoline demand elasticity has declined in absolute value to near zero since the 1980s. The elasticity was stable near −0.3 until the end of 2014. It rose to about −0.2 in 2015–16, but has remained stable since 2016. Gasoline demand is more responsive in states with lower personal income, higher unemployment rates and lower urban population shares. There is no evidence for an asymmetry in the elasticity with respect to positive and negative gasoline price shocks. We illustrate how these elasticity estimates inform the recent policy debate about the impact of gasoline tax holidays on consumers’ discretionary income and about the demand destruction from the spike in gasoline prices after the invasion of Ukraine.

Do state-dependent local projections asymptotically recover the population responses of macroeconomic aggregates to structural shocks? The answer to this question depends on how the state of the economy is determined and on the magnitude of the shocks. When the state is exogenous, the local projection estimator recovers the population response regardless of the shock size. When the state depends on macroeconomic shocks, as is common in empirical work, local projections only recover the conditional response to an infinitesimal shock, but not the responses to larger shocks of interest in many applications. Simulations suggest that impulse responses may be off by as much as 82 percent and fiscal multipliers by as much as 40 percent.

Since the 1970s, exports and imports of manufactured goods have been the engine of international trade and much of that trade relies on container shipping. This paper introduces a new monthly index of the volume of container trade to and from North America. Incorporating this index into a structural macroeconomic VAR model facilitates the identification of shocks to domestic U.S. demand as well as foreign demand for U.S. manufactured goods. We show that, unlike in the Great Recession, the primary determinant of the U.S. economic contraction in early 2020 was a sharp drop in domestic demand. Although detrended data for personal consumption expenditures and manufacturing output suggest that the U.S. economy has recovered to near 90% of pre-pandemic levels as of March 2021, our structural VAR model shows that the component of manufacturing output driven by domestic demand had only recovered to 57% of pre-pandemic levels and that of real personal consumption only to 78%. The difference is mainly accounted for by unexpected reductions in frictions in the container shipping market.