Banca de DEFESA: VINICIUS TEODORO SCHER

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
STUDENT : VINICIUS TEODORO SCHER
DATE: 18/02/2022
TIME: 08:30
LOCAL: video conferência
TITLE:

ESSAYS ON BOUNDED TIME SERIES ANALYSIS

KEY WORDS:

βARMA, Bootstrap, Hydrological data, Information criterion, Model selection, Monte Carlo simulation, Portmanteau test, Time series

PAGES: 57
BIG AREA: Ciências Exatas e da Terra
AREA: Probabilidade e Estatística
SUMMARY:

Two important steps in time series analysis are model selection and diagnostic analysis. We address the issue of performing diagnostic analysis through portmanteau testing inferences using time series data that assume values in the standard unit interval. Our focus lies in the class of beta autoregressive moving average (βARMA) models. In particular, we wish to test the goodness-of-fit of such models. We consider several testing criteria that have been proposed for Gaussian time series models and two new tests that were recently introduced in the literature. We derive the asymptotic null distribution of the two new test statistics in two different scenarios, namely: when the tests are applied to an observed time series and when they are applied to residuals from a fitted βARMA model. It is worth noticing that our results imply the asymptotic validity of standard portmanteau tests in the class of ARMA models that are, under the null hypothesis, asymptotically equivalent to the two new tests. We use Monte Carlo simulation to assess the relative merits of the different portmanteau tests when used with fitted βARMA. The simulation results we present show that the new tests are typically more powerful than a well known test whose test statistic is also based on residual partial autocorrelations. Overall, the two new tests perform quite well. We also model the dynamics of the proportion of stocked hydroelectric energy in South of Brazil. The results show that the βARMA model outperforms three alternative models and an exponential smoothing algorithm. We also consider the issue of performing model selection with double bounded time series. We evaluate the effectiveness of βARMA model selection strategies based on different information criteria. The numerical evidence for autoregressive, moving average, and mixed autoregressive and moving average models shows that, overall, a bootstrap-based model selection criterion is the best performer. An empirical application which we present and discuss shows that the most accurate out-of-sample forecasts are obtained using bootstrap-based model selection.

BANKING MEMBERS:
Externa à Instituição - AIRLANE PEREIRA ALENCAR
Presidente - 1279737 - FRANCISCO CRIBARI NETO
Interna - 1196642 - PATRICIA LEONE ESPINHEIRA OSPINA
Externo à Instituição - PEDRO ALBERTO MORETTIN
Interno - 1651445 - RAYDONAL OSPINA MARTINEZ