Measuring the effect of oil prices on wheat futures prices

Cartwright, Phillip A.; Riabko, Natalija

doi:10.1016/j.ribaf.2014.04.002

Cited by 26 publications

(13 citation statements)

References 19 publications

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“…However, the literature has not reached a consensus around the role that oil prices play in agricultural commodity prices. Some studies document price transmission from crude oil to agricultural commodity prices (Leibtag 2009; Nazlioglu 2011; Myers et al 2014; Cartwright and Riabko 2015; Fernandez‐Perez, Frijns, and Tourani‐Rad 2016), while others find no association (Nazlioglu and Soytas 2011; Reboredo 2012; Fowowe 2016).…”

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confidence: 99%

“…Our results illustrate new stylized facts that underlie mixed findings in the literature—given the horizon‐based heterogeneity we document, it is natural that empirical findings will be sensitive to sampling frequency, temporal aggregation, and econometric specification. Indeed, Cartwright and Riabko (2015) find that oil and wheat prices are linked but that the relationship is unstable and sensitive to temporal aggregation. Nazlioglu and Soytas (2011) find no short‐ or long‐run relationship between oil prices and agricultural commodity prices in Turkey using monthly data.…”

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confidence: 99%

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Oil Price Pass through to Agricultural Commodities^†

Lundberg¹,

Skolrud²,

Adrangi³

et al. 2020

American J Agri Economics

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Energy represents an important share of production costs for many agricultural commodities. Previous studies have found mixed evidence of a pass‐through relationship between oil prices and agricultural commodity prices, a relationship that has the potential to disrupt farm‐level decision making. We propose that these mixed findings are in part due to heterogeneity in the pass‐through relationship across time horizons. We use a new wavelet‐based regression approach to explore horizon‐based heterogeneity in the relationship between oil and agricultural commodity prices. We find strong evidence of heterogeneity across time horizons and commodities. We develop a stylized model of agricultural production and show that agricultural contracts can generate price stickiness that leads to heterogeneity in input price pass through over different horizons. We also find evidence that recent technological shifts have led to a structural change in this horizon‐based heterogeneity.

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confidence: 99%

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confidence: 99%

Oil Price Pass through to Agricultural Commodities^†

Lundberg¹,

Skolrud²,

Adrangi³

et al. 2020

American J Agri Economics

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“…Recall the random regression model equation 4, = ̅ + (13) Where Y represents NTx1 vector of observations, X represents NTxNK matrix of independent variables, ̅ is a vector of unknown fixed parameters, and the NTx1 vector of random error terms. From equation (13) we have that the i-th cross-section unit = ̅ + + = 1,2, … ,…”

Section: Minque For Rcr Modelmentioning

confidence: 99%

“…where = + and the variables are defined as the same definitions and assumptions as in the previous section. from (13), and (14) we can rewrite the model as = + 1 1 + 2 2 + ⋯ + + +1 +1 +∈ (15) where is × diagonal matrix whose corresponding diagonal elements of = , = 1,2, … . is × 1 random vector has a multivariate normal distribution with mean ̅ and covariance matrix ∆.…”

Section: Minque For Rcr Modelmentioning

confidence: 99%

Comparing MINQUE and IAUE estimates of variance components of a random coefficient model

Badr¹,

Youssef²,

Abu-zinadah³

2017

ams

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An alternative way to estimate the coefficient variance in the Swamy's RCR model has been derived using Minimum Norm Quadratic Estimation (MINQUE), and the Iteration Almost Unbiased Estimator (IAUE) methods. The estimators' performance in the RCR model are examined in Monte Carlo study. The Monte Carlo study provides some insight into how well the RCR model performs in small, medium, and large samples in the case of random, mixed, and fixed coefficient regression. We found that using MINQUE method to estimate the coefficient variance has reduce the probability of having negative variance comparing with the Swamy method. IAUE method was superior, since it gives zero percent of negative 2728 Souha K. Badr et al. variance and has a low variation and a low bias in estimation coefficient parameters, even in case of fixed coefficients.

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“…(Domanski and Heath, 2007;Brunetti and Reiffen, 2014). As a consequence, prices are no longer determined by just supply and demand, but also by the risk appetite of index investors and their diversification strategies, and they are more and more correlated with oil prices (Tang and Xiong, 2012;Cartwright and Riabko, 2015). Büyüksahin and Robe (2014) use the CFTC's microeconomic data and show that the correlation between stock market indexes and commodity indexes increases together with the relative weight of hedge funds on both markets.…”

Section: State Of the Artmentioning

confidence: 99%