Publication Type
Working Paper
Version
publishedVersion
Publication Date
7-2010
Abstract
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 the test works reasonably well through Monte Carlo simulations and apply it to an automobile insurance data set. Our empirical results consolidate Chiappori and Salanié’s (2000) findings that there is no evidence for the presence of asymmetric information in the French automobile insurance market.
Keywords
Asymmetric information, Automobile insurance, Conditional independence, Distributional misspecification, Functional misspecification, Nonlinearity, Nonparametric test
Discipline
Econometrics | Statistics and Probability
Research Areas
Econometrics
First Page
1
Last Page
33
Citation
SU, Liangjun and SPINDLER, Martin.
Nonparametric Testing for Asymmetric Information. (2010). 1-33.
Available at: https://ink.library.smu.edu.sg/soe_research/1266
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.