Chapter 9 Forecasting Techniques
Instructions: Please submit your work in one single Excel file with one tab/worksheet for each problem.
Forecasting Models for Stationary Time Series
- (40 points) The Excel file Closing Stock Prices provides data for four stocks and the Dow Jones Industrial Average over a one-month period.
- Develop a spreadsheet model for forecasting each of the stock prices using a simple two-period and three-period moving average. Compute MAD to determine which model is better?
- Develop a spreadsheet model for forecasting each of the stock prices using simple exponential smoothing with a smoothing constant of 0.1 and 0.5. Compute MAD to determine which model is better?
Forecasting Models for Time Series with a Linear Trend
- (20 points) Consider the data in the Excel file Consumer Price Index.
- Use double exponential smoothing to find forecasts for the next two years.
- Use simple linear regression to find forecasts for the next two years.
Forecasting Time Series with Seasonality
- (20 points) Develop a multiple regression model with categorical variables that incorporate seasonality for forecasting for the data in the Excel file Coal Consumption.
Regression Forecasting with Causal Variables
- (20 points) Data in the Excel file Microprocessor Data shows the demand for one type of chip used in industrial equipment from a small manufacturer.
- Construct a chart of the data. What appears to happen when a new chip is introduced?
- Develop a causal regression model to forecast demand that includes both time and the introduction of a new chip as explanatory variables.
