7 research outputs found

    Measuring Competition between Non-Food and Food Demand on World Grain Markets : Is Biofuel Production Compatible with Pressure for Food Production ?

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    The flow of agricultural products between countries is conditioned by several factors including domestic and trade policy tools for the main competing exporters countries, and macroeconomic variables (such as real income per capita, rate of population growth,etc). Important structural changes are occurring on world agricultural markets that will have an impact on the long term competitiveness of countries and regions. These changes include developments in biofuels production linked to policy incentives, and the rapid growth in income and population numbers in some developing countries (such as India and China). An important issue is to identify the factors that are going to modify the balance between the supply and demand for agricultural products in the long term. In this paper, we look the example of arable crops. These markets allow an interesting analysis since they are directly concerned with the evolution of biofuels. One important question is to measure the competition between food demand and non food demand. We use a partial equilibrium model that focuses on world arable crop markets, the World Econometric Modeling of Arable Crops. The aim of the model is to produce annual market projections over a medium-term perspective and to simulate the impact of alternative national and international agricultural policy reforms for the main arable crops. The results of the simulations performed show that even if incentives to produce of biofuels have strong impacts on world markets, other factors such as changes in the assumptions of concerning the growth of emerging countries are also of great importance since the world cereal and oilseed markets as much are just bymodeling, econometric, partial equilibrium, land uses, biofuels, Demand and Price Analysis, International Relations/Trade,

    What are the long-term drivers of food prices? Investigating improvements in the accuracy of prediction intervals for the forecast of food prices

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    Over the last few years, the prices of the main agricultural raw materials have been highly volatile. The situation is unprecedented, both in the magnitude of the upward and downward volatility observed, and in the number of agricultural commodities affected. Various factors are contributing to these contrasting shifts: the role of emerging countries, changing dietary habits, an increase in energy demand related to the boom in biofuels, adverse weather conditions and speculation. In this paper we try to capture long-term relationships between crop prices and crude oil price using a partial equilibrium and times series method. The study finds little empirical evidence that the crude oil price have a significant influence on the variation of major vegetable crops pricesPartial equilibrium modeling, Forecasting cointegration, Demand and Price Analysis, Q11, Q13, Q42,

    Factor Analysis for Multiple Testing (FAMT): An R Package for Large-Scale Significance Testing under Dependence

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    The R package FAMT (factor analysis for multiple testing) provides a powerful method for large-scale significance testing under dependence. It is especially designed to select differentially expressed genes in microarray data when the correlation structure among gene expressions is strong. Indeed, this method reduces the negative impact of dependence on the multiple testing procedures by modeling the common information shared by all the variables using a factor analysis structure. New test statistics for general linear contrasts are deduced, taking advantage of the common factor structure to reduce correlation and consequently the variance of error rates. Thus, the FAMT method shows improvements with respect to most of the usual methods regarding the non discovery rate and the control of the false discovery rate (FDR). The steps of this procedure, each of them corresponding to R functions, are illustrated in this paper by two microarray data analyses. We first present how to import the gene ex- pression data, the covariates and gene annotations. The second step includes the choice of the optimal number of factors, the factor model fitting, and provides a list of selected genes according to a preset FDR control level. Finally, diagnostic plots are provided to help the user interpret the factors using available external information on either genes or arrays.

    Measuring Competition between Non-Food and Food Demand on World Grain Markets : Is Biofuel Production Compatible with Pressure for Food Production ?

    No full text
    The flow of agricultural products between countries is conditioned by several factors including domestic and trade policy tools for the main competing exporters countries, and macroeconomic variables (such as real income per capita, rate of population growth,etc). Important structural changes are occurring on world agricultural markets that will have an impact on the long term competitiveness of countries and regions. These changes include developments in biofuels production linked to policy incentives, and the rapid growth in income and population numbers in some developing countries (such as India and China). An important issue is to identify the factors that are going to modify the balance between the supply and demand for agricultural products in the long term. In this paper, we look the example of arable crops. These markets allow an interesting analysis since they are directly concerned with the evolution of biofuels. One important question is to measure the competition between food demand and non food demand. We use a partial equilibrium model that focuses on world arable crop markets, the World Econometric Modeling of Arable Crops. The aim of the model is to produce annual market projections over a medium-term perspective and to simulate the impact of alternative national and international agricultural policy reforms for the main arable crops. The results of the simulations performed show that even if incentives to produce of biofuels have strong impacts on world markets, other factors such as changes in the assumptions of concerning the growth of emerging countries are also of great importance since the world cereal and oilseed markets as much are just b

    What are the long-term drivers of food prices? Investigating improvements in the accuracy of prediction intervals for the forecast of food prices

    No full text
    Over the last few years, the prices of the main agricultural raw materials have been highly volatile. The situation is unprecedented, both in the magnitude of the upward and downward volatility observed, and in the number of agricultural commodities affected. Various factors are contributing to these contrasting shifts: the role of emerging countries, changing dietary habits, an increase in energy demand related to the boom in biofuels, adverse weather conditions and speculation. In this paper we try to capture long-term relationships between crop prices and crude oil price using a partial equilibrium and times series method. The study finds little empirical evidence that the crude oil price have a significant influence on the variation of major vegetable crops price

    Factor analysis for multiple testing (FAMT): an R package for large-scale significance testing under dependence

    No full text
    The R package FAMT (factor analysis for multiple testing) provides a powerful method for large-scale significance testing under dependence. It is especially designed to select differentially expressed genes in microarray data when the correlation structure among gene expressions is strong. Indeed, this method reduces the negative impact of dependence on the multiple testing procedures by modeling the common information shared by all the variables using a factor analysis structure. New test statistics for general linear contrasts are deduced, taking advantage of the common factor structure to reduce correlation and consequently the variance of error rates. Thus, the FAMT method shows improvements with respect to most of the usual methods regarding the non discovery rate and the control of the false discovery rate (FDR). The steps of this procedure, each of them corresponding to R functions, are illustrated in this paper by two microarray data analyses. We first present how to import the gene expression data, the covariates and gene annotations. The second step includes the choice of the optimal number of factors, the factor model fitting, and provides a list of selected gene according to a preset FDR control level. Finally, diagnostic plots are provided to help the user interpret the factors using a vailable external information on either genes or arrays
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