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A Comprehensive Evaluation of USDA Cotton Forecasts AgEcon
Isengildina-Massa, Olga; MacDonald, Stephen; Xie, Ran.
This study evaluates all USDA cotton supply and demand estimates for the United States and China (including unpublished price forecasts) from 1985/86 through 2009/10 for accuracy and efficiency. Results reveal that at every stage of the forecasting cycle forecast smoothing was the most widespread and persistent type of inefficiency observed in most U.S. variables. Correlation with past errors indicated the tendency to repeat past errors in most cases. Tendency to overestimate growth was also found. Bias was uncommon and limited to several cases of overestimation of China’s exports and U.S. price and underestimation of China’s domestic use. While forecasts of China’s imports and endings stocks improved, U.S. price and ending stock forecast errors became...
Tipo: Article Palavras-chave: Cotton; Forecast accuracy; Forecast efficiency; Forecast evaluation; Forecast smoothing; USDA forecasts; Agricultural Finance; Crop Production/Industries.
Ano: 2012 URL: http://purl.umn.edu/122314
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How Do Restaurants Benefit from Various Components of a Regional Promotion Campaign? AgEcon
Xie, Ran; Isengildina-Massa, Olga; Carpio, Carlos E..
This study analyses the economic value of various components of the Certified South Carolina Grown Campaign from the perspective of participating restaurants. A stated-preference choice experiment was conducted as part of the restaurant survey to estimate the willingness to pay (WTP) for each campaign component using a mixed logit model. Individual level WTP was calculated in order to explore the relationship between WTP and characteristics of restaurants. Results indicate that three existing components--Labeling, Multimedia Advertising, and "Fresh on the Menu" have significant positive economic value.
Tipo: Presentation Palavras-chave: WTP; Mixed Logit Model; SUR Model; Discrete Choice Model; Agribusiness; Agricultural and Food Policy; Political Economy; Production Economics; Research Methods/ Statistical Methods.
Ano: 2012 URL: http://purl.umn.edu/123921
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