Beta autoregressive moving average models

• Autores: Andréa V. Rocha, Francisco Cribari Neto
• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 18, Nº. 3, 2009, págs. 529-545
• Idioma: inglés
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• Resumen

  • We build upon the class of beta regressions introduced by Ferrari and Cribari-Neto (J. Appl. Stat. 31:799–815, 2004) to propose a dynamic model for continuous random variates that assume values in the standard unit interval (0,1). The proposed βARMA model includes both autoregressive and moving average dynamics, and also includes a set of regressors. We discuss parameter estimation, hypothesis testing, goodness-of-fit assessment and forecasting. In particular, we give closed-form expressions for the score function and for Fisher’s information matrix. An application that uses real data is presented and discussed.