Estimate a regression model with trend & season dummies to forecast the next year sales

Estimate a regression model with trend & season dummies to forecast the next year sales:

1)      Use estimated regression coefficients to compute in-sample forecasts for sales

2)      For the forecasted quarterly sales in years 2006 and 2007 calculate:

• Error
• Squared error
• Absolute % error
• The numerator of Thiel’s U statistic
• The denominator of Thiel’s U statistic

3)      Calculate the following error metrics: mean error (ME), mean squared error (MSE), root mean squared error (RMSE), mean absolute percent error (MAPE), and Thiel’s U statistic

4)      Compute sales forecast in the year 2008

Estimate another regression model, but this time use M2 Index, non-farm activity index, and oil prices as predictors to forecast the next year sales.  Repeat steps 1) – 4) and calculate error metrics for years 2006-2007 and sales forecast in the year 2008.

Compute sales forecasts using additive Holt-Winters model. Repeat steps 1) – 3) and calculate error metrics for years 2006-2007.  Calculate the optimal alpha, beta, and gamma parameters of the model using MS Excel Solver so as to minimize RMSE.  Compute sales forecast in the year 2008.

Compare the error metrics (ME, RMSE, MAPE, Thiel’s U) of the four models.

Construct a line plot showing the actual quarterly sales and forecast by these models.

Write a management report (using the required format) suggesting and justifying an appropriate decision regarding the sales forecast.  In your report, analyze the patterns of the quarterly time series; discuss the properties of each forecasting model and their relevance to predicting the series; select the best forecasting model for the series; make your recommendations supported by arguments which are further supported by references to model results and tables or figures in your report.  Independent outside research is encouraged to provide relevant background information and/ or to provide more support for your arguments.

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