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Accounting for short samples and heterogeneous experience in rating crop insurance

Julia I. Borman (North Carolina State University, Raleigh, North Carolina, USA)
Barry K. Goodwin (Agricultural and Resource Economics, North Carolina State University, Raleigh, North Carolina, USA)
Keith H. Coble (Agricultural Economics, Mississippi State University, Mississippi State, Mississippi, USA)
Thomas O. Knight (Agricultural and Applied Economics, Texas Tech University, Lubbock, Texas, USA)
Rod Rejesus (Agricultural and Resource Economics, North Carolina State University, Raleigh, North Carolina, USA)

Agricultural Finance Review

ISSN: 0002-1466

Article publication date: 3 May 2013

Abstract

Purpose

The purpose of this paper is to be an academic inquiry into rating issues confronted by the US Federal Crop Insurance program stemming from changes in participation rates as well as the weighting of data to reflect longer‐run weather patterns.

Design/methodology/approach

The authors investigate two specific approaches that differ from those adopted by the Risk Management Agency, building upon standard maximum likelihood and Bayesian estimation techniques that consider parametric densities for the loss‐cost ratio.

Findings

Both approaches indicate that incorporating weights into the priors for Bayesian estimation can inform the distribution.

Originality/value

In most cases, the authors' results indicate that including weighting into priors for Bayesian estimation implied lower premium rates than found using standard methods.

Keywords

Citation

Borman, J.I., Goodwin, B.K., Coble, K.H., Knight, T.O. and Rejesus, R. (2013), "Accounting for short samples and heterogeneous experience in rating crop insurance", Agricultural Finance Review, Vol. 73 No. 1, pp. 88-101. https://doi.org/10.1108/00021461311321339

Publisher

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Emerald Group Publishing Limited

Copyright © 2013, Emerald Group Publishing Limited