Can expert knowledge compensate for data scarcity in crop insurance pricing?
研究在农作物数据稀缺时，利用专家知识结合贝叶斯框架估计损失分布，发现专家知识能降低参数不确定性并正确调整保险费率。
Although there is an increasing interest in area yield insurance in many developing countries, crop data scarcity hinders its implementation by imposing higher premiums. Expert knowledge has been considered a valuable information source to augment limited data in insurance pricing. This article investigates whether the use of expert knowledge can mitigate model risk arising from insufficient statistical data. We adopt a Bayesian framework that allows for the combination of scarce crop data, expert knowledge and weather information, to estimate the loss distribution. We find that expert knowledge reduces the parameter uncertainty and changes the insurance premium in the correct direction.