Missing (Completely?) At Random: Lessons from Insurance Studies
A dilemma frequently faced by empirical researchers is whether they should keep observations without complete information in the analysis. Assuming missingness is not biased in any perceivable direction, most studies use a complete case analysis approach, whereby only observations with complete information are kept for empirical estimation. However, the literature on statistics (e.g., Little and Rubin 2002) suggests that potential biases may arise from such practice, especially if missing data are not missing completely at random (MCAR). When there are missing data, Littles MCAR test (1988) can be performed to reveal whether imputation methods are necessary to minimize the problems arising from incomplete data. We take two recently studied insurance data sets as examples to show that missing data issues can be better handled.
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Volume (Year): 3 (2009)
Issue (Month): 2 (April)
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References listed on IDEAS
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- Joan T. Schmit & Jia-Hsing Yeh, 2003. "An Economic Analysis of Auto Compensation Systems: Choice Experiences From New Jersey and Pennsylvania," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 70(4), pages 601-628.
- Browne, Mark J & Puelz, Robert, 1999. "The Effect of Legal Rules on the Value of Economic and Non-economic Damages and the Decision to File," Journal of Risk and Uncertainty, Springer, vol. 18(2), pages 189-213, August.
- Little, Roderick J A, 1985. "A Note about Models for Selectivity Bias," Econometrica, Econometric Society, vol. 53(6), pages 1469-1474, November.
- Horton N.J. & Lipsitz S.R. & Parzen M., 2003. "A Potential for Bias When Rounding in Multiple Imputation," The American Statistician, American Statistical Association, vol. 57, pages 229-232, November.
- Steven B. Caudill & Mercedes Ayuso & Montserrat Guillén, 2005. "Fraud Detection Using a Multinomial Logit Model With Missing Information," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 72(4), pages 539-550.
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