University of Kentucky Researchers Have Published New Data on Econometrics (Combining Predictions of Auto Insurance Claims): Economics – Econometrics

2022 JUL 12 (NewsRx) -- By a News Reporter-Staff News Editor at Economics Daily Report -- Fresh data on econometrics are presented in a new report. According to news reporting out of Lexington, Kentucky, by NewsRx editors, research stated, “This paper aims to better predict highly skewed auto insurance claims by combining candidate predictions. We analyze a version of the Kangaroo Auto Insurance company data and study the effects of combining different methods using five measures of prediction accuracy.”

Our news editors obtained a quote from the research from University of Kentucky: “The results show the following. First, when there is an outstanding (in terms of Gini Index) prediction among the candidates, the ‘forecast combination puzzle’ phenomenon disappears. The simple average method performs much worse than the more sophisticated model combination methods, indicating that combining different methods could help us avoid performance degradation. Second, the choice of the prediction accuracy measure is crucial in defining the best candidate prediction for ‘low frequency and high severity’ (LFHS) data. For example, mean square error (MSE) does not distinguish well between model combination methods, as the values are close. Third, the performances of different model combination methods can differ drastically. We propose using a new model combination method, named ARM-Tweedie, for such LFHS data; it benefits from an optimal rate of convergence and exhibits a desirable performance in several measures for the Kangaroo data.”

According to the news editors, the research concluded: “Fourth, overall, model combination methods improve the prediction accuracy for auto insurance claim costs. In particular, Adaptive Regression by Mixing (ARM), ARM-Tweedie, and constrained Linear Regression can improve forecast performance when there are only weak learners or when no dominant learner exists.”

For more information on this research see: Combining Predictions of Auto Insurance Claims. Econometrics, 2022,10(19):19. (Econometrics - http://www.mdpi.com/journal/econometrics). The publisher for Econometrics is MDPI AG.

A free version of this journal article is available at https://doi.org/10.3390/econometrics10020019.

Our news journalists report that additional information may be obtained by contacting Chenglong Ye, Dr. Bing Zhang Department of Statistics, University of Kentucky, 317 Multidisciplinary Science Building, 725 Rose St., Lexington, KY 40536, United States. Additional authors for this research include Lin Zhang, Mingxuan Han, Yanjia Yu, Bingxin Zhao, Yuhong Yang.

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