Macroeconomic Shocks in an Oil Market VAR

Abstract

This paper studies oil market and other macroeconomic shocks in a structural vector autoregression with sign restrictions. It introduces a new indicator for oil demand, and uniquely, performs a sign restriction set-up with a penalty function approach in an oil market vector autoregression. The model also allows for macroeconomic shocks in the US. The results underline the importance of the source of an oil shock for its macroeconomic consequences. Oil supply shocks have been less relevant in driving real oil prices, and had less of an e¤ect on US in ‡ation than demand shocks. Overall, the e¤ects of oil shocks on US real activity have been relatively limited, as also highlighted by a counterfactual experiment of recent oil market developments.

Made in Mexico: Energy reform and manufacturing growth

“…This is also observed in other developed economies Kilian (2009). andMelolinna (2012) decompose demand and supply oil shocks, and conclude the supply shocks have a similar effect on output.…”

“…US Industrial production is a stronger predictor of growth in Mexico than headline U.S. growth, in particular of manufacturing growth, given the degree of integration of manufacturing production in both countries.11Blanchard and Gali (2007),Kilian (2009) andMelolinna (2012) use three to six variable VARS that include GDP, unemployment, wages, cost of capital, and oil market variables. Here, we are interested in the effect of relative prices on output, and therefore we do not include aggregate employment or capital accumulation measures.…”

The effect of the global supply chain and oil prices on the inflation rates in advanced economies and emerging markets

“…Peersman and Van Robays (2012) and Gupta, Ivanov & Choi (2021) documented that the inflation rate is driven by oil supply shock. In contrast, Melolinna (2012) noted that oil demand shock mainly affects inflation. Several recent scholars demonstrated co‐movement evidence between oil price and inflation at time‐varying and depending on time‐frequency using wavelet coherence methods (Su et al, 2020; Xiang et al, 2021).…”