|Forecasting plays an important role in business planning and decision-making. This Ph.D.-level course discusses linear 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 integrated moving average models; (3) model building and data analysis; (4) prediction and forecasting evaluation; (5) asymptotic theory for estimation including unit-root theory; (6) transfer function (distributed lag) models; (7) regression model with time series errors; (8) structural changes and outlier detection; (9) state-space models and Kalman filter; and (10) nonlinear models if time permits.
|Software: R and SCA will be introduced to perform data analysis, but students can use other software.|
|Homework assignments (30%), mid-term (30%), and final exam (40%).|
|Business 41901, or instructor consent.
Description and/or course criteria last updated: 6/09
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