Suchergebnisse

Why and When Should Quantile Regression Be Used?- A Case of Study: Modelling Energy Markets by the Means of Quantile Regression -- Quantile Regression: A Methodological Overview -- Cross-Sectional Quantile Regression -- Time Series Quantile Regression -- Goodness of Fit in Quantile Regression Models -- Novel Approaches in Quantile Regression -- What Have We Learned from Quantile Regression? Implications for Economics and Finance -- Appendix: Programs for Quantile Regression and Implementation in R. .

Knowledge of trends in life expectancy is of major importance for policy planning. It is also a key indicator for assessing future development of life insurance products, substantiality of existing retirement schemes, and long‐term care for the elderly. This article examines the feasibility of decomposing age‐gender‐specific accidental and natural mortality rates. We study this decomposition by using the Lee and Carter model. In particular, we fit the Poisson log‐bilinear version of this model proposed by Wilmoth and Brouhns et al. to historical (1975–1998) Spanish mortality rates. In addition, by using the model introduced by Wilmoth and Valkonen we analyze mortality‐gender differentials for accidental and natural rates. We present aggregated life expectancy forecasts compared with those constructed using nondecomposed mortality rates.

AbstractMost automobile insurance databases contain a large number of policyholders with zero claims. This high frequency of zeros may reflect the fact that some insureds make little use of their vehicle, or that they do not wish to make a claim for small accidents in order to avoid an increase in their premium, but it might also be because of good driving. We analyze information on exposure to risk and driving habits using telematics data from a pay‐as‐you‐drive sample of insureds. We include distance traveled per year as part of an offset in a zero‐inflated Poisson model to predict the excess of zeros. We show the existence of a learning effect for large values of distance traveled, so that longer driving should result in higher premiums, but there should be a discount for drivers who accumulate longer distances over time due to the increased proportion of zero claims. We confirm that speed limit violations and driving in urban areas increase the expected number of accident claims. We discuss how telematics information can be used to design better insurance and to improve traffic safety.