Please use this identifier to cite or link to this item: https://repositorio.insper.edu.br/handle/11224/5943
Type: Working Paper
Title: Particle Learning for Fat-tailed Distributions
Author: Lopes, Hedibert Freitas
Polson, Nicholas G.
Publication Date: 2014
Abstract: It is well-known that parameter estimates and forecasts are sensitive to assump tions about the tail behavior of the error distribution. In this paper we develop an approach to sequential inference that also simultaneously estimates the tail of the accompanying error distribution. Our simulation-based approach models errors with a tν-distribution and, as new data arrives, we sequentially compute the marginal posterior distribution of the tail thickness. Our method naturally incorporates fat-tailed error distributions and can be extended to other data features such as stochastic volatility. We show that the sequential Bayes factor provides an optimal test of fat-tails versus normality. We provide an empir ical and theoretical analysis of the rate of learning of tail thickness under a default Jeffreys prior. We illustrate our sequential methodology on the British pound/US dollar daily exchange rate data and on data from the 2008-2009 credit crisis using daily S&P500 returns. Our method naturally extends to multivariate and dynamic panel data.
Keywords (english terms): Bayesian Inference
MCMC
Kullback-Leibler
Dynamic Panel Data
Credit Crisis
Language: Inglês
CNPq Area: Ciências Exatas e da Terra
Copyright: O INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DO USUÁRIO VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITOR
Appears in Collections:Coleção Insper Working Papers

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