Modelo beta autorregressivo de médias móveis: critérios de seleção e aplicações

• Ano de defesa: 2015
• Autor(a) principal: Guerra, Renata Rojas
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; BR; Engenharia de Produção; UFSM; Programa de Pós-Graduação em Engenharia de Produção
• Palavras-chave em Português: Crédito consignado; Critérios de seleção de modelos; Inadimplência de crédito; Modelo β; ARMA; Simulação de Monte Carlo; β; ARMA model; Credit delinquency; Model selection criteria; Monte Carlo simulation; Payroll loan; CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO
• Link de acesso: http://repositorio.ufsm.br/handle/1/8336

Time series modeling and forecasting has many applicability in scientific and technological researchs. Specifically about variables restricted to the interval (0; 1), which includes rates and proportions, the classical regression models could not be suitable because they assume normality. In this context, Rocha and Cribari-Neto (2009) proposed the beta autoregressive moving average (βARMA) model. It admits that the variable of interest is beta distributed. The beta distribution is more flexible than the normal distribution and also assumes that de dependent variable is restricted to the interval (0; 1). Through βARMA is possible to obtain results closer to the nature of the data. But just choose the better parametric model does not guarantee the accuracy of the fitted model. To identify the lags is also relevant to ensure the accuracy of the adjusted model. It is in this purpose that the model selection criteria, or information criteria, were developed. They compare the explanatory capacity of a group of models and select, among this group, the model which minimizes the information loss. In this context, this paper aims to evaluate by Monte Carlo simulations the performance of different selection criteria in βARMA model. Considering several scenarios and sample sizes, the selection criteria evaluated was AIC, BIC, HQ, AICc, BICc and HQc. The results indicate that BICc, HQ and HQc had the better performance identifying the true model among the candidate models. Using the selection criteria indicated by the simulation study, were also adjusted βARMA models to real data. It were considered the credit delinquency and the relationship between payroll loan and individual credit, both variables are from national financial system. It was adjusted the classical ARIMA models too. This models were compared with βARMA in applications. For both variables was found a reasonable proximity between the original data and the predicted by the models, with advantage for βARMA, as much inside as outside the sample.