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Beta seasonal autoregressive moving average models. (English) Zbl 07192698
Summary: In this paper, we introduce the class of beta seasonal autoregressive moving average \(( \beta\) SARMA) models for modelling and forecasting time series data that assume values in the standard unit interval. It generalizes the class of beta autoregressive moving average models [A. V. Rocha and the third author, Test 18, No. 3, 529–545 (2009; Zbl 1203.62160)] by incorporating seasonal dynamics to the model dynamic structure. Besides introducing the new class of models, we develop parameter estimation, hypothesis testing inference, and diagnostic analysis tools. We also discuss out-of-sample forecasting. In particular, we provide closed-form expressions for the conditional score vector and for the conditional Fisher information matrix. We also evaluate the finite sample performances of conditional maximum likelihood estimators and white noise tests using Monte Carlo simulations. An empirical application is presented and discussed.
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