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Regression for Asymmetric Information Problem: The Comparison of Different Methods

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  • Chun-Ting Liu
  • Chih-Ping Yu

Abstract

In previous decades, seminal theories were advanced addressing the problems of asymmetric information. These scholarly works confirmed a significant positive correlation between insurance coverage and risk in insurance markets. We showed that when regression models were used to analyze the asymmetric information problem, the sample selection bias and chosen regression methods could produce distinct results. Moreover, samples at different quantiles may lead to considerably different levels of information asymmetry. The present research suggests that quantile regression models be employed to better reveal the situation of the insurance market and reduce estimation errors.

Suggested Citation

  • Chun-Ting Liu & Chih-Ping Yu, 2024. "Regression for Asymmetric Information Problem: The Comparison of Different Methods," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 60(10), pages 2126-2146, August.
    • Handle: RePEc:mes:emfitr:v:60:y:2024:i:10:p:2126-2146
    DOI: 10.1080/1540496X.2023.2300634
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