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On Prediction of Future Insurance Claims When the Model Is Uncertain

13 Pages Posted: 12 Dec 2016 Last revised: 22 Feb 2017

Liang Hong

Robert Morris University - Department of Mathematics

Todd Kuffner

Washington University in St. Louis

Ryan Martin

North Carolina State University - Department of Statistics

Date Written: December 10, 2016

Abstract

Predictive modeling is arguably one of the most important tasks actuaries face in their day-to-day work. In practice, actuaries may have a number of reasonable models to consider, all of which will provide different predictions. The most common strategy is to first use some kind of model selection tool to select a “best model,” and then use that model to make predictions. However, there is reason to be concerned about the use of the classical distribution theory to develop predictions because these ignore the selection effect. Since accuracy of predictions is crucial to the insurer’s pricing and solvency, care is needed to develop valid prediction methods. In this paper, we undertake an investigation of the effects of model selection on the validity of classical prediction tools and make some recommendations for practitioners.

Keywords: Bootstrap; Post-Selection Inference; Predictive Distribution; Regression; Variable Selection

JEL Classification: C51; C52; G22

Suggested Citation

Hong, Liang and Kuffner, Todd and Martin, Ryan, On Prediction of Future Insurance Claims When the Model Is Uncertain (December 10, 2016). Available at SSRN: https://ssrn.com/abstract=2883574 or http://dx.doi.org/10.2139/ssrn.2883574

Liang Hong (Contact Author)

Robert Morris University - Department of Mathematics ( email )

6001 University Blvd.
Moon Township, PA 15108
United States
(412)397-4024 (Phone)

HOME PAGE: http://https://sites.google.com/a/rmu.edu/lhong/

Todd Kuffner

Washington University in St. Louis ( email )

One Brookings Drive
Saint Louis, MO 63130-4899
United States

Ryan Martin

North Carolina State University - Department of Statistics ( email )

Raleigh, NC 27695-8203
United States

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