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World oil prices and agricultural commodity prices: Evidence from an emerging market
Saban Nazlioglu,Ugur Soytas +1 more
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TLDR
This article examined the short and long-run interdependence between world oil prices, lira-dollar exchange rate, and individual agricultural commodity prices (wheat, maize, cotton, soybeans, and sunflower) in Turkey.About:
This article is published in Energy Economics.The article was published on 2011-05-01. It has received 267 citations till now. The article focuses on the topics: Exchange rate.read more
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Volatility spillover between oil and agricultural commodity markets
TL;DR: In this article, the authors examined volatility transmission between oil and selected agricultural commodity prices (wheat, corn, soybeans, and sugar) and applied the newly developed causality in variance test and impulse response functions to daily data from 01 January 1986 to 21 March 2011.
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Oil price, agricultural commodity prices, and the dollar: A panel cointegration and causality analysis
Saban Nazlioglu,Ugur Soytas +1 more
TL;DR: In this article, the authors examined the dynamic relationship between world oil prices and twenty four world agricultural commodity prices accounting for changes in the relative strength of US dollar in a panel setting.
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How does oil price volatility affect non-energy commodity markets?
TL;DR: In this paper, the influence of the crude oil market on non-energy commodity markets before and after the 2008 financial crisis was investigated by constructing a bivariate EGARCH model with time-varying correlation construction.
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World oil and agricultural commodity prices: Evidence from nonlinear causality
TL;DR: In this article, the authors extended the literature on the oil-agricultural commodity prices nexus, which particularly concentrates on nonlinear causal relationships between the world oil and three key agricultural commodity prices (corn, soybeans, and wheat).
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Biofuel-related price transmission literature: A review
Teresa Serra,David Zilberman +1 more
TL;DR: An extensive review of the rapidly growing biofuel-related time-series literature is carried out in this paper, which concludes that energy prices drive long-run agricultural price levels and that instability in energy markets is transferred to food markets.
References
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Journal ArticleDOI
Distribution of the Estimators for Autoregressive Time Series with a Unit Root
David A. Dickey,Wayne A. Fuller +1 more
TL;DR: In this article, the limit distributions of the estimator of p and of the regression t test are derived under the assumption that p = ± 1, where p is a fixed constant and t is a sequence of independent normal random variables.
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Testing for a Unit Root in Time Series Regression
TL;DR: In this article, the authors proposed new tests for detecting the presence of a unit root in quite general time series models, which accommodate models with a fitted drift and a time trend so that they may be used to discriminate between unit root nonstationarity and stationarity about a deterministic trend.
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Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?
TL;DR: In this paper, a test of the null hypothesis that an observable series is stationary around a deterministic trend is proposed, where the series is expressed as the sum of deterministic trends, random walks, and stationary error.
Journal ArticleDOI
Statistical inference in vector autoregressions with possibly integrated processes
Hiro Y. Toda,Taku Yamamoto +1 more
TL;DR: In this paper, the authors show how to estimate VAR's formulated in levels and test general restrictions on the parameter matrices even if the processes may be integrated or cointegrated of an arbitrary order.
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Generalized Impulse Response Analysis in Linear Multivariate Models
H.Hashem Pesaran,Yongcheol Shin +1 more
TL;DR: This paper proposed a generalized impulse response analysis for unrestricted vector autoregressive (VAR) and cointegrated VAR models, which does not require orthogonalization of shocks and is invariant to the ordering of the variables in the VAR.
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