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In agricultural insurance practice, risk and indemnity payment are often incurred from individual farmer’s yield. However, high administration cost and data scarcity are simultaneously quite often seen, which form huge burdens for insurers to adequately rate insurance products. Under this circumstance, some methods that could be used to estimate farmers’ yields, in particular, their distributions, are urgently needed. Among these methods, a so called PERT fitting technique often prevails due to its simplicity which only requires very little knowledge about the yield history, that is frequently implemented by both academics and practitioners. However, the very limited information used would sometimes cause severe bias, in other words, the reliability of this method is yet to be examined. In this paper, I used Monte Carlo experiments to test the robustness of PERT fittings under Var and CTE risk measures in different scenarios. The result proves that PERT method is indeed robust and trustworthy.
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