Statistics

ST782 - Time Series Analysis: Time Domain

• Prerequisites: ST 512 and ST 522
• Term & Frequency: Spring (every two years)
• Student Audience: graduate students in statistics and other fields
• Credit: 3 credits
• Recent Texts: None
• Recent Instructors: Donald Martin
• Background and Goals: In this class we study fundamental tools and concepts for time series analysis in the time domain. Models for stationary and nonstationary univariate series are examined, as well as estimation inference for coefficients of models. Autocorrelation and partial autocorrelation and their use in identification of time series models are considered. Extensions are given to multivariate series.
• Content: Stationarity, autocorrelation, partial autocorrelation function, autoregressive moving average (ARMA) processes, vector processes, prediction, order in probability, convergence, central limit theorems, estimation of ARMA models, ARIMA models and other nonstationary and seasonal models, regression with time series errors, ARCH models.
• Alternatives: None
• Subsequent Courses: None

SP 2013 Sections: