Time Series and Forecasting 3,

4 Credits

This course introduces the student to the statistical analysis of time series data and useful models: autocorrelation, stationarity, trend removal, and seasonal adjustment, basic time series models like AR, MA, ARMA; estimation, forecasting, and GARCH models; multivariate models, and factor models. The course emphasizes the main ideas and the most popular and widely used methods, and the use of a computer to practice the methods. Knowledge of scientific programming in a language such as R required. Prerequisites: ( MATH 264 or MATH 312 ) and ( MATH 263 or MATH 309 ) Attribute/Distribution: Q