This course introduces basic concepts and methods for time series analysis and forecasting, as well as related statistical software and real-world data applications. Topics include autocorrelation function, partial autocorrelation function, extended autocorrelation function, autoregressive-moving-average models, models for seasonal time series, unit-root test, integrated processes, distributed lag models, and transfer function models. Optional topics may include conditional heteroscedastic models and the Kalman filter.
This course will be offered in academic years ending in even numbers.