Reference no: EM131862948

Assignment

1. Go to Minitab "Stat" the Time Series then select the exponential smoothing method you wish to use. Rule -- for any data with only trend and cycle -- use Double Exponential Smoothing, -- for any data with seasonality you must use Winters Exponential Smoothing. Be sure to tell me why you selected the exponential smoothing model type by noting X variable trend cycle and seasonality. (refer to previous time series plots and ACFs--discussed in class).

2. Show the model that you chose that includes the plot of the actuals and forecast, smoothing constants and fit period error measures. (You may copy and paste from Minitab). Do not show failed model or attempts in this assignment document. Show only the one best model for this method for each X variable selected. So, if you have 4 X variables then you should have 4 exponential smoothing models and forecasts.

3. Run residual analysis to determine what if any T, C and S is not picked up by each the model. Determine if the systematic error remaining is significant by showing the residual time series plot and commenting on and T, C or S that you note. In addition determine if the residual variation includes significant T, C or S information with ACFs and LBQ values copied from Minitab. Show the residual distribution and mean with a Histogram of the model fit period residuals. Comment on whether the residuals are random or not.

4. Plot the 8 quarter forecast along with any data shortfall (missing recent values) to note how well the model tracks with the actual data. (Do not use a hold out for any of these forecasts)

5. Calculate and present the fit period error measures (RMSE and MAPE) and the forecast period error measures (RMSE and MAPE) to determine model accuracy for each variable. You will need this later to compare with other forecast methods for the X variables. This is critical to prevent carrying error into the final multiple regression model.

6. Plot the forecast appended to the fit period data for each variable to check the forecast for reasonableness. Copy and paste the plot in the assignment Word document. Conclude with a statement of the usefulness of this forecast for your company's strategic plan. Is it reasonable? If not, why not?

Show your work and submit it to the Chapter 4 Assignment 6 Dropbox.

This assignment addresses forecasting your selected Y data (dependent variable) using an exponential smoothing technique. Note: Do not use the X (independent) variables in this exercise. Use only one exponential smoothing method -- the best that applies. Do not use any other forecasting techniques in this assignment. Turn in only the one best model that you develop.

(Remember-- 1. Do not show failed models in business reports. Share your failures with your family if you wish and not with your boss or instructor.and 2. Never use Y hold out data observations in any forecast model.)

1. Tell me why you selected the appropriate exponential smoothing method by commenting on your Y data characteristics. (you should use a time series plot and autocorrelations to do this)

2. Apply the appropriate exponential smoothing forecast technique to your Y variable excluding the last two years of data (8 quarter hold out period). Show the Y data, fitted values and residuals in excel format and show your exponential smoothing model coefficients. (Find the correct coefficient and not just use the default values.)

3. Evaluate the "Goodness To Fit" using at least two error measures -- RMSE and MAPE.

4. Check the "Fit" period residual mean proximity to zero and randomness with a time series plot; check the residual time series plot and autocorrelations (ACFs) for trend, cycle and seasonality.

5. Evaluate the residuals for the "Fit" period by indicating the residual distribution using a histogram (normal or not and random or not)

6. Comment on the acceptability of the model's ability to pick up the systematic variation in your Fit period actual data.

7. Develop a two year quarterly forecast (for the hold out period). Evaluate the "Accuracy" of the forecast for the "hold out period" using RMSE and MAPE error measures used from forecast period residuals and comment them.

8. Do the forecast period residuals seem to be random relative to the hold out period data? Check the forecast period time series plot of the residuals.

9. Did the error measures get worse, remain the same or get better from the fit to the hold out period? Do you think the forecast accuracy is acceptable?

Show your work and graphs in a Word document. Make sure that you comment on statistics and graphs relevant to answering the above questions. DO NOT leave statistics and graphs stranded. If you show something write about it. Note that this work will become part of your class project so do a good job on it.

Attachment:- Macroeconomic-Data.rar