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Faculty of Economics

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Buch-Kromann, T., Guillen, M., Linton, O. and Nielsen, J.P.

Multivariate density estimation using dimension reducing information and tail flattening transformations

Insurance: Mathematics and Economics

Abstract: We propose a nonparametric multiplicative bias corrected transformation estimator designed for heavy tailed data. The multiplicative correction is based on prior knowledge and has a dimension reducing effect at the same time as the original dimension of the estimation problem is retained. Adding a tail flattening transformation improves the estimation significantly–particularly in the tail–and provides significant graphical advantages by allowing the density estimation to be visualized in a simple way. The combined method is demonstrated on a fire insurance data set and in a data-driven simulation study.

JEL Codes: G22

Author links: Oliver Linton  

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Papers and Publications

Recent Publications

Elliott, M. and Golub, B. A network approach to public goods accepted, Journal of Political Economy [2018]

Jochmans, K., and Weidner, M. Fixed-Effect Regressions on Network Data Econometrica, forthcoming [2019]

Carvalho, V. M. and Grassi, B. Large Firm Dynamics and the Business Cycle American Economic Review [2019]