J
Jayson L. Lusk
Researcher at Purdue University
Publications - 395
Citations - 16842
Jayson L. Lusk is an academic researcher from Purdue University. The author has contributed to research in topics: Willingness to pay & Common value auction. The author has an hindex of 63, co-authored 385 publications receiving 14769 citations. Previous affiliations of Jayson L. Lusk include San Diego State University & West Texas A&M University.
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Effects of meat recalls on futures market prices
Jayson L. Lusk,Ted C. Schroeder +1 more
TL;DR: This article examined the impact of beef and pork recalls on nearby daily live cattle and lean hog futures market prices, and found that medium sized beef and large pork recalls that are a serious health concern have a marginally negative impact on short-term live cattle futures prices, respectively, and concluded that if there is any systematic significant change in demand due to meat recalls, it likely occurs over an extended period of time and only in certain cases does it noticeably affect daily futures prices.
Posted ContentDOI
Effect of Publicly Released Quality Information for US Hard Red Winter Wheat on Mexican Millers' Welfare
TL;DR: In this paper, the value of information to Mexican millers is measured by the difference of the flour mill surplus and compensating variation, and the value is defined as the sum of the difference between the two values.
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Transatlantic Differences in Consumer Preferences
TL;DR: This article conducted a consumer study in France, Germany, the UK, and the US and found that French consumers are willing to pay more than US consumers for beef from cattle not administered hormones.
Posted ContentDOI
Hypothesis testing using numerous approximating functional forms
TL;DR: The authors found that composite forecasts are more accurate than a single model's forecast, while hypothesis tests using information from numerous models are, on average, more accurate in the sense of lower Type I and Type II errors than hypothesis tests with a single models.
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Reply: Ranking Crop Yield Models
TL;DR: In this article, the authors proposed an out-of-sample-log-likelihood function (OSLLF) approach to evaluate crop yield distributions by determining how well they describe the distribution of out-ofthe-sample yields.