Binary choice models for rare events data: a crop insurance fraud application
Yufei Jin, Roderick M. Rejesus, Bertis B. Little
Abstract
Yufei Jin, Roderick M. Rejesus, Bertis B. Little
Abstract
This study implements a recently proposed score test that could help guide insurance fraud researchers in deciding whether to use a logit or a probit model in predicting insurance fraud probabilities, especially when the occurrence of ones in the dependent variable is much less than zeros. The test is easily implemented in a crop insurance fraud context and seems to be a promising method that could be applicable to analysing and detecting potentially fraudulent claims in various lines of insurance.
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This study implements a recently proposed score test that could help guide insurance fraud researchers in deciding whether to use a logit or a probit model in predicting insurance fraud probabilities, especially when the occurrence of ones in the dependent variable is much less than zeros. The test is easily implemented in a crop insurance fraud context and seems to be a promising method that could be applicable to analysing and detecting potentially fraudulent claims in various lines of insurance.
Key concepts: Crop insurance, Actuarial science, Context (archaeology), Logit, Probit, Econometrics, Insurance policy, Economics