Estimating Farm Level Multivariate Yield Distribution Using Nonparametric Methods

Modeling crop yield distributions has been an important topic in agricultural production and risk analysis, and nonparametric methods have gained attention for their flexibility in describing the shapes of yield density functions. In this article, we apply a nonparametric method to model joint yield distributions based on farm-level data for multiple crops, and also provide a way of simulation for univariate and bivariate distributions. The results show that the nonparametric models, both univariate and bivariate, are estimated quite well compared to the original samples, and the simulated empirical distributions also preserve the attributes of the original samples at a reasonable level. This article provides a feasible way of using multivariate nonparametric methods in further risk and insurance analysis.


Issue Date:
2008
Publication Type:
Conference Paper/ Presentation
PURL Identifier:
http://purl.umn.edu/6509
Total Pages:
31
Series Statement:
Selected Paper
464062




 Record created 2017-04-01, last modified 2017-08-23

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