scispace - formally typeset
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.

Papers
More filters
Posted Content

Effects of meat recalls on futures market prices

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.
Journal ArticleDOI

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.
Journal ArticleDOI

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.