BAN430 will provide students with an overview of time series methods used in business administration and economics for the purpose of forecasting and forecasting evaluation, with emphasis on applied forecasting. The main time series topics that will be covered:
- The decomposition of Time Series (season, cycle, trend)
- Linear models (ARIMA), including exponential smoothing
- Dynamic regression models
- Forecasting, including Judgmental forecasts
- Volatility forecasting with GARCH models
Many decisions, especially in economics and business, depend on future values of variables of interest. Therefore, it is necessary to forecast these variables as accurately as possible. For example, theoretically, the value of a stock depends on future dividends, and if you can make better forecasts of dividends, then you can price stocks more correctly. Forecasting macroeconomic variables is essential because they influence tax revenues, which are important for a government's spending. Bad forecasts may have substantial negative effects on the real economy through bad decisions. Retail businesses may want to forecast sales volumes, which depend not only on the season but also on advertising, to maintain an optimal inventory level. The purpose of this course is to give students tools for making forecasts and an understanding of the modelling and forecasting of economic variables. In workshops and lectures, examples are included to demonstrate how forecasting can inform decisions with implications for efficiency and sustainability.