2008/05/12 by Vitor A. Ozaki, Vitor Augusto Ozaki, Sujit K. Ghosh +2 · 60 citations
Agricultural and Biological Sciences · Economics, Econometrics and Finance · Engineering · Mathematics · #Agricultural Economics and Policy #Agricultural risk and resilience #Agriculture #Autocorrelation #Bayesian probability #Computer science #Crop insurance #Data set #Econometrics #Economics #Economics of Agriculture and Food Markets #Engineering #Estimation #Geography #Mathematics #Panel data #Range (aeronautics) #Statistical model #Statistics
paper · open access · doi:10.1111/j.1467-8276.2008.01153.x
published in American Journal of Agricultural Economics 90(4), 951-961 (Wiley)
openalex publication_date 2008/05/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/02
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.