Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks

By Christiane Baumeister, James D. Hamilton. The American Economic Review, 2019

771 citations recorded by the source corpus.

Traditional approaches to structural vector autoregressions (VARs) can be viewed as special cases of Bayesian inference arising from very strong prior beliefs. These methods can be generalized with a less restrictive formulation that incorporates uncertainty about the identifying assumptions themselves. We use this approach to revisit the importance of shocks to oil supply and demand. Supply disruptions turn out to be a bigger factor in historical oil price movements and inventory accumulation a smaller factor than implied by earlier estimates. Supply shocks lead to a reduction in global economic activity after a significant lag, whereas shocks to oil demand do not. (JEL C32, L71, Q35, Q43)

Subfields: Economics and Econometrics, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/8f9138644fd89540737553d32235f9bbaa4760bb

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Citations in the corpus

The impact of oil-market shocks on stock returns in major oil-exporting countries (2018, background): “Baumeister and Hamilton (2015), among others, recently criticized this aspect of Kilian’s (2009) modelling framework. The main contribution of our paper is to investigate the impact of oil-market shocks on stock prices in oil exporting countries. We follow Kilian’s (2009) two-stage approach. However”
The Macroeconomic Effects of Oil Supply News: Evidence from OPEC Announcements (2021, neutral): “The oil price expectations are from Baumeister and Kilian (2017) and the inflation expectations from the Michigan Surveys of Consumers (median).”
A Model of the Fed's View on Inflation (2017, neutral): “…the two diﬀerent measures of global activity: (i) the Baltic Dry Index and index of global cargo shipments, initially proposed by Kilian (2009) but taken in levels; (ii) the measure of Global Industrial Production proposed by Baumeister and Hamilton (2019) and based on the OECD methodology.”
Energy Markets and Global Economic Conditions (2020, background): “On the other hand, Baumeister et al. (2017) find that the unobserved component stochastic volatility model does great for forecasting retail gasoline prices, especially at longer horizons. The question is thus whether a more accurate modeling of time-varying uncertainty leads to improvements in fore”
The global effects of Covid-19-induced uncertainty (2020, positive): “shocks’’ = VARX with Miranda-Agrippino and Rey’s (2020) US monetary policy shocks as exogenous variable; ‘‘Oil shocks’’ = VARX with Baumeister and Hamilton’s (2019) oil supply shocks as exogenous variable; ‘‘Set identification’’ = uncertainty shock identified with a combination of sign and event res”
Commodity price volatility and the economic uncertainty of pandemics (2020, background): “For instance, the world industrial production index (Baumeister and Hamilton, 2019) has fallen by 4.”
Oil price elasticities and oil price fluctuations (2016, methodology): “Rows 3 and 4 show that adding oil inventories to our VAR, as done by Kilian and Murphy (2014) and Baumeister and Hamilton (2017) , does not materially change our results. Row 5 considers the role of the metal price index. Dropping metal prices from our specification enhances the contribution of oil ”
Inference in Structural Vector Autoregressions identified with an external instrument (2020, methodology): “This paper shows that confidence sets for impulse response coefficients constructed using Fieller (1944) and Anderson and Rubin (1949) methods are valid when external instruments are weak and asymptotically coincide with the usual confidence sets when instruments are strong and the model is just ide”
Inference in structural vector autoregressions when the identifying assumptions are not fully believed: Re-evaluating the role of monetary policy in economic fluctuations (2018, positive): “As noted by Baumeister and Hamilton (2015, 2017), using Bayesian priors to assign plausibility to different magnitudes within the identified set can also be regarded as a strict generalization of full identification.”