TitleOn order identification of time series models and its applications
NameChen, Shuhao (author), Chen, Rong (chair), Dicker, Lee (internal member), Xie, Minge (internal member), Min, Wanli (outside member), Rutgers University, Graduate School - New Brunswick,
SubjectStatistics and Biostatistics,
Seasonal variations (Economics),
DescriptionMy thesis focuses on the order identification schemes of the widely-used time series model - Autoregressive Integrated Moving-Average (ARIMA) model and the applications of the order determination methods. The first part investigates the impact of dependent but uncorrelated innovations (errors) on the traditional autoregressive integrated moving average (ARIMA) model order determination schemes such as autocorrelation function (ACF), partial autocorrelation function (PACF), extended autocorrelation function (EACF) and unit-root test. We also propose a new order determination scheme to address those impacts and can be used to time series sequences with uncorrelated innovations. In the second part, a unified approach for the tentative specification of both the seasonal and nonseasonal orders of general multiplicative seasonal model is proposed. This new approach has the advantages of determining the seasonal and nonseasonal orders simultaneously and automatically. In the third part, a hierarchical model approach is presented for predicting the end-of-day stock trading volume (total daily volume). It effectively combines two sources of information: the trading volume already accumulated from the beginning of the trading day to the time of prediction, and the historical daily trading volume dynamics.
NoteIncludes bibliographical references
Noteby Shuhao Chen
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.