Considering macroeconomic indicators in the food before fuel nexus

Qiu, Cheng; Colson, Gregory; Escalante, Cesar L.; Wetzstein, Michael E.

doi:10.1016/j.eneco.2012.08.018

Cited by 76 publications

(38 citation statements)

References 24 publications

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“…For a review of the literature see Qiu et al (2011). Recent literature employing a VECM, vector autoregressive (VAR) model, or other models assessing volatility generally concludes that energy markets have a short-run impact on the food market, but no long-run impacts (McPhail, 2011;McPhail et al, 2012;Nazlioglu et al, 2013;Qiu et al, 2012;Saghaian, 2010). Exceptions are Serra et al (2011) who find both a short-and long-run relations between ethanol and corn prices and the conclusion by Serra and Zilberman (2013) inferring that the literature indicates a long-run relation between energy and agricultural commodity prices.…”

Section: Literature Reviewmentioning

confidence: 99%

Drought, ethanol, and livestock

et al. 2015

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Section: Literature Reviewmentioning

confidence: 99%

Drought, ethanol, and livestock

et al. 2015

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“…However, the US biofuel industry has attracted more attention than the EU biofuels sector and the Brazilian biofuel markets [50][51][52][53][54][55][56][57][58][59][60][61][62]. In particular, the link between the sugar and energy markets and between ethanol and crude oil/gasoline markets was examined by four authors.…”

Section: Food-fuel Price Interdependence: a Brief Literature Reviewmentioning

confidence: 99%

Interdependencies between Biofuel, Fuel and Food Prices: The Case of the Brazilian Ethanol Market

2016

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Abstract:Brazil is currently the world's largest sugar producer and exporter, as well as the world's largest producer and consumer of sugarcane ethanol as a transportation fuel. The growth of this market originates from a combination of government policies and technological change, in both the sugarcane ethanol processing sector and the manufacture of flex-fuel vehicles. In recent years however, ethanol production has been questioned due to its possible impact on food prices. The present paper aims to explore the impact of Brazilian ethanol prices on sugar and gasoline prices. The relationships between a times series of these prices are investigated using a Vector Error Correction Model (VECM), supported by Granger Causality tests. In addition, Impulse Response Functions (IRFs) and Forecast Error Variance Decompositions (FEVD) are computed in order to investigate the dynamic interrelationships within these series. Our results suggest that ethanol prices are affected by both food and fuel prices, but that there is no strong evidence that changes in ethanol prices have an impact on food prices.

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“…In a new generation of studies, following Kilian [2009a]'s structural VAR model based on oil price decomposition methodology, Qiu et al [2012] use the structural VAR model to decompose supply-demand structural shock effects on corn and fuels prices. The fuels prices include oil, ethanol and gasoline during the 1994-2010 period.…”

Section: Literature Reviewmentioning

confidence: 99%

How is Volatility in Commodity Markets Linked to Oil Price Shocks?

2015

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This study investigates the effects of oil price shocks on volatility of selected agricultural and metal commodities. To achieve this goal, we decompose an oil price shock to its underlying components, including macroeconomics and oil specific shocks. The applied methodology is the structural vector autoregressive (SVAR) model and the time span is from April 1983 to December 2013. The investigation is divided into two subsamples, before and after 2006 for agricultures taking into account the 2006-2008 food crisis, and before and after 2008 for metals considering the recent global financial crisis. The validity of time divisions is confirmed by historical decomposition accomplishment. We find that, based on impulse response functions, the response of volatility of each commodity to an oil price shock differs significantly depending on the underlying cause of the shock for the both pre and post-crisis periods. moreover, according to variance decomposition the explanatory power of oil shocks becomes stronger after the crisis. The different responses of commodities are described in detail by investigating market characteristics in each period.

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Considering macroeconomic indicators in the food before fuel nexus

Cited by 76 publications

References 24 publications

Drought, ethanol, and livestock

Drought, ethanol, and livestock

Interdependencies between Biofuel, Fuel and Food Prices: The Case of the Brazilian Ethanol Market

How is Volatility in Commodity Markets Linked to Oil Price Shocks?

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