Course: STAT 33500=BUSF 41910
Title: Time-Series Analysis For Forecasting and Model Building
Instructor(s): Jeffrey R. Russell
Class Schedule: Sec 01: F 1:30 PM–4:20 PM in Harper Center 3B
Textbook(s): Hamilton, Time Series Analysis, 1st edition.
Description: Forecasting plays an important role in business planning and decision-making. This Ph.D.-level course discusses time series models that have been widely used in business and economic data analysis and forecasting. Both theory and methods of the models are discussed. Real examples are used throughout the course to illustrate applications. The topics covered include: (1) stationary and unit-root non-stationary processes; (2) linear dynamic models, including Autoregressive Moving Average models; (3) model building and data analysis; (4) prediction and forecasting evaluation; (5) asymptotic theory for estimation including unit-root theory; (6) models for time varying volatility; (7) models for time varying correlation including Dynamic Conditional Correlation and time varying factor models.; (9) state-space models and Kalman filter; and (10) models for high frequency data. Course description is subject to change. Please visit the Booth portal and search via the course search tool for the most up to date information:
Prerequisite(s): BUSF 41901/STAT 32400 or instructor consent