Title: Test inferences and link function selection in dynamic beta modeling of seasonal hydro-environmental time series with temporary abnormal regimes

Wednesday, October 23, from 3:30 p.m. to 5:00 p.m. (Rio de Janeiro local time)

Speaker: Francisco Cribari Neto (UFPE)

Local: Laboratório de Sistemas Estocásticos (LSE), Sala I-044-B, Centro de Tecnologia - UFRJ.

Abstract: We consider the modeling and forecasting of hydro-environmental time series subject to seasonal fluctuations and prolonged droughts. Abnormally dry periods have become more frequent as a result of climate change. We use a class of dynamic beta models which is tailored for doubly-bounded data. We examine two important aspects of this class of models: the accuracy of hypothesis tests based on asymptotic approximations and the choice of link function. In particular, we show that two commonly used tests can yield inaccurate inferences if the estimation of the null model is done on the basis of the maximum number of individual conditional log-likelihoods, and we introduce a new model selection criterion for selecting the model's link function. Based on tests and the new criterion, we model the useful volumes of three Brazilian hydroelectric power plant water reservoirs. These time series exhibit seasonal fluctuations and contain abnormally dry periods due to intense and prolonged droughts. In-sample predictions and out-of-sample forecasts are produced and compared to those obtained with well known alternative approaches.

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