A Flexible Parametric Family for the Modeling and Simulation of Yield Distributions

Ramírez, Octavio A.; McDonald, Tanya U.; Carpio, Carlos E.

doi:10.1017/s1074070800003473

Cited by 11 publications

(13 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The yield distributions used for this research were selected from those estimated by Ramirez, McDonald, and Carpio (2010) using data from the University of Illinois Endowment Farms (Sherrick et al 2004). The dataset contains yields from 26 representative com farms located in twelve counties across that state.…”

Section: Selection Of Yield Distributionsmentioning

confidence: 99%

“…The dataset contains yields from 26 representative com farms located in twelve counties across that state. Ramirez, McDonald, and Carpio (2010) use this data to estimate models for those 26 yield distributions that are as realistic as possible. To this end, they utilize a system of probability distributions that has sufficient flexibility to parametrically model any empirically possible distributional shape with a high level of accuracy.…”

Section: Selection Of Yield Distributionsmentioning

confidence: 99%

“…Specifically, the analysis is based on yield distributions previously estimated on the basis of one of the most comprehensive farm-level datasets in the United States (Sherrick et al 2004) and recently developed parametric modeling procedures. Since these procedures are flexible enough to accommodate a wide variety of distributional shapes (Ramirez, McDonald, and Carpio 2010, Ramirez and McDonald 2006a, Ramirez, Misra, and Field 2003, the estimated distributions should sufficiently resemble the true underlying yield densities to make the analyses realistic. The estimated distributions are then assumed to be the true data-generating processes and used to simulate yield datasets for the analyses.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Can Crop Insurance Premiums Be Reliably Estimated?

Ramírez

Carpio

Rejesus

2011

Agric. resour. econ. rev.

Self Cite

View full text Add to dashboard Cite

This paper develops and applies a methodology to assess the accuracy of historical loss-cost rating procedures, similar to those used by the U.S. Department of Agriculture's Risk Management Agency (RMA), versus alternative parametric premium estimation methods. It finds that the accuracy of loss-cost procedures leaves much to be desired, but can be markedly improved through the use of alternative methods and increased farm-level yield sample sizes. Evidence suggests that the high degree of inaccuracy in crop insurance premium estimations through historical loss-cost procedures identified in the paper might be a major factor behind the need for substantial government subsidies to keep the program solvent.

show abstract

Section: Selection Of Yield Distributionsmentioning

confidence: 99%

Section: Selection Of Yield Distributionsmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Can Crop Insurance Premiums Be Reliably Estimated?

Ramírez

Carpio

Rejesus

2011

Agric. resour. econ. rev.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, Remirez et al (2010) introduced a system of distributions that can span the entire mean-varianceskewness-kurtosis space and assessed that the distribution system potentially be a comprehension parametric crop yield model. Zhu et al (2011) simultaneously estimated the time trend effects and the parameters of the parametric yield distribution to take account of technological progress and consequential changes in yield risk.…”

Section: Introductionmentioning

confidence: 99%

Estimating bivariate yield distributions and crop insurance premiums using nonparametric methods

Zheng¹,

Wang²,

Shi³

2014

Applied Economics

View full text Add to dashboard Cite

Modelling crop yield distribution is crucial in crop insurance premium setting. The correlation between different crop yields due to rotations or systemic risks requires estimation of joint yield distribution for multiple crops. In this article, we apply a nonparametric method to estimate bivariate yield distributions using farm-level yield data of wheat and corn in Shandong Province in China. Then, the simulated yields are used to evaluate the expected indemnity of one traditional and one hypothetical crop insurance programme. Our results reveal that the nonparametric bivariate method is very flexible in shaping the yield probability density functions to estimate local idiosyncrasies and correlation between two crops. It is also feasible to simulate the nonparametric yield distributions at a satisfying level of accuracy. The simulation results show that the hypothetical two-crop insurance contract can be more affordable to farmers than traditional individual crop insurance contracts.

show abstract

“…The three general procedures that have been proposed for this purpose are non-parametric, semi-parametric and parametric in nature (Ramirez et al, 2010). The non-parametric approaches, such as those used by the RMA, are free of a functional form assumption and thus generally more flexible.…”

mentioning

confidence: 99%

Premium estimation inaccuracy and the actuarial performance of the US crop insurance program

Ramírez

Carpio

2012

Agricultural Finance Review

Self Cite

View full text Add to dashboard Cite

Purpose -The purpose of this paper is to explore the impact of the levels of inaccuracy associated with three different premium estimation methods, one of which attempts to mimic the protocol currently used by the Risk Management Agency (RMA), on the actuarial performance of the US crop insurance program. Design/methodology/approach -The analyses are conducted using empirically-grounded simulation and other computational methods, under various plausible assumptions about the producer's risk aversion behavior and knowledge of his/her actuarially fair premium. Findings -Regardless of the assumed producer knowledge and behavior, it is concluded that the persistently high government subsidy levels required to keep the program solvent could be solely explained by the inaccuracy in the RMA's premium estimates. In other words, the observed need for large subsidies does not necessarily imply that the program is systematically favoring less efficient farmers or particular crops or production areas. Also, contrary to the commonly accepted "adverse selection" argument, it is shown that farmers having more information about their actuarially fair premiums than the insurer is not the reason why high subsidies are needed. Actuarial performance, however, could be improved by using the more elaborate methods exemplified in the paper, as well as larger sample sizes for premium estimation. Originality/value -The paper provides conclusions and recommendations that could substantially reduce the amount of public subsidies needed to keep the US crop insurance program solvent.

show abstract

A Flexible Parametric Family for the Modeling and Simulation of Yield Distributions

Cited by 11 publications

References 12 publications

Can Crop Insurance Premiums Be Reliably Estimated?

Can Crop Insurance Premiums Be Reliably Estimated?

Estimating bivariate yield distributions and crop insurance premiums using nonparametric methods

Premium estimation inaccuracy and the actuarial performance of the US crop insurance program

Contact Info

Product

Resources

About