Bimodal Birnbaum–Saunders generalized autoregressive score model

Rodney V. Fonseca and Francisco Cribari-Neto

Journal of Applied Statistics, 2018, vol. 45, issue 14, 2585-2606

Abstract: Time series models based on the Birnbaum–Saunders ( $ \mathcal{BS} $ BS) distribution have not received much attention in the literature, there being only a few articles that address such models. In the present paper, we propose a generalized autoregressive score (GAS) model based on a bimodal Birnbaum–Saunders law. The proposed model, denoted by GBS2-GAS, generalizes an existing time series $ \mathcal{BS} $ BS model. We discuss conditional maximum likelihood parameter estimation, hypothesis testing inference, residual analysis and develop prediction intervals for the GBS2-GAS model. Additionally, we provide analytical expressions for the score vector and for the Hessian matrix. Two empirical applications, involving financial and hydrological data, are presented and discussed.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:45:y:2018:i:14:p:2585-2606

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DOI: 10.1080/02664763.2018.1428734

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