SPATIO-TEMPORAL MODELING OF AGRICULTURAL YIELD DATA WITH AN APPLICATION TO PRICING CROP INSURANCE CONTRACTS.
This article presents a statistical model of agricultural yield data based on a set of hierarchical Bayesian models that allows joint modeling of temporal and spatial autocorrelation. This method captures a comprehensive range of the various uncertainties involved in predicting crop insurance premium rates as opposed to the more traditional ad hoc, two-stage methods that are typically based on independent estimation and prediction. A panel data set of county-average yield data was analyzed for 290 counties in the State of Paraná (Brazil) for the period of 1990 through 2002. Posterior predictive criteria are used to evaluate different model specifications. This article provides substantial improvements in the statistical and actuarial methods often applied to the calculation of insurance premium rates. These improvements are especially relevant to situations where data are limited.
Ozaki
Vitor A
VA
Vitor A. Ozaki and Ricardo Shirota are professors, respectively, in the Department of Exact Sciences and Economics, Business, and Sociology, University of São Paulo; Sujit K. Ghosh and Barry K. Goodwin are full and William Neal Reynolds Distinguished Professor, respectively, in the Department of Statistics and Agricultural and Resource Economics, North Carolina State University.
Ghosh
Sujit K
SK
Goodwin
Barry K
BK
Shirota
Ricardo
R