Approximate Bayesian Computations to fit and compare insurance loss models

Pierre-Olivier Goffard and Patrick J. Laub

Insurance: Mathematics and Economics, 2021, vol. 100, issue C, 350-371

Abstract: Approximate Bayesian Computation (abc) is a statistical learning technique to calibrate and select models by comparing observed data to simulated data. This technique bypasses the use of the likelihood and requires only the ability to generate synthetic data from the models of interest. We apply abc to fit and compare insurance loss models using aggregated data. A state-of-the-art abc implementation in Python is proposed. It uses sequential Monte Carlo to sample from the posterior distribution and the Wasserstein distance to compare the observed and synthetic data.

Keywords: Bayesian statistics; Approximate Bayesian computation; Likelihood-free inference; Statistical claim modeling; Sequential Monte Carlo; Wasserstein distance; Compound distribution (search for similar items in EconPapers)
JEL-codes: C02 C11 C63 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:100:y:2021:i:c:p:350-371

DOI: 10.1016/j.insmatheco.2021.06.002

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