EC-451 Economic Forecasting
This course introduces the student to forecasting methods using time-series data with economic and financial data. Time-series models commonly used in forecasting include the autoregressive moving average (ARMA) model for stationary series and the autoregressive integrated moving average (ARIMA) model for nonstationary series. These models will be presented together with the estimation methods then used in applications. Statistical methods designed to evaluate, compare, and improve forecasting performance by combining different types of forecasts are also discussed and illustrated using a widely-used program such as R.
Prerequisite
Student has completed any of the following course(s) STATS 240 - Introduction to Statistics, STATS 250 - Applied Statistics