Nonparametric Testing for Asymmetric Information
提出一种新的非参数检验方法，用于检测保险市场中的不对称信息，通过蒙特卡洛模拟和汽车保险、长期护理保险数据集验证，发现法国汽车保险市场无不对称信息，而美国长期护理保险市场存在与风险偏好相关的不对称信息。
Asymmetric information is an important phenomenon in many markets and in particular in insurance markets. Testing for asymmetric information has become a very important issue in the literature in the last two decades. Almost all testing procedures that are used in empirical studies are parametric, which may yield misleading conclusions in the case of misspecification of either functional or distributional relationships among the variables of interest. Motivated by the literature on testing conditional independence, we propose a new nonparametric test for asymmetric information, which is applicable in a variety of situations. We demonstrate that the test works reasonably well through Monte Carlo simulations and apply it to an automobile insurance dataset and a long-term care insurance (LTCI) dataset. Our empirical results consolidate Chiappori and Salanié's findings that there is no evidence for the presence of asymmetric information in the French automobile insurance market. While Finkelstein and McGarry found no positive correlation between risk and coverage in the LTCI market in the United States, our test detects asymmetric information using only the information that is available to the insurance company, and our investigation of the source of asymmetric information suggests some sort of asymmetric information that is related to risk preferences as opposed to risk types and thus lends support to Finkelstein and McGarry.