Evaluate goodness to fit using rmse and mape error measures


Assignment:

a) Perform Time Series Decomposition on your project Y variable excluding the hold out period. Show me the smoothed Trend Values, Smoothed Cycle Values and Seasonal Indexes.

note that you can create your own cycle factors for the forecast period and apply them to a multiplicative Minitab result.

b) Show the seasonal indices and develop a one year time series plot of them. Do they indicate strong seasonality? How can you tell?

c) Evaluate the "Goodness To Fit" using RMSE and MAPE error measures .

d) Evaluate the residuals for the "Fit" period by indicating the residual distribution (random or not). Use a fit period residual time series plot, residuals ACFs and a histogram to determine if the Fit period residuals are random. If the residuals are not random state if you detect any trend, cycle and seasonality.

e) Develop a two year quarterly forecast (for the hold out period) using the time series decomposition model you evaluated in c) above.

f) Evaluate the "Accuracy" of the forecast for the "hold out period" using the RMSE and MAPE measures used in part b) and comment them.

Did the error measures increase, remain the same or decrease from the "Fit" to "Hold Out" or forecast period?

Show your work and graphs in a Word document. Make sure that you comment on statistics and graphs relevant to answering the above questions. This will be the decomposition portion of your class project. Be sure that you only submit one Word document in this assignment. With your first initial, last name and Eco309 Assignment 8 in the file name.

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Microeconomics: Evaluate goodness to fit using rmse and mape error measures
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