# an additive seasonal regression model

1. Fit the data with an additive seasonal regression model with second order polynomial trend component. (This process is described in section 11.20 of SMDA for quarterly data where p=4. In this case, the data is monthly so you’d need to use p=12.)

a. What is the equation of the model you estimated?

b. For each value in the sample data, plot the estimated CPI values produced by this model against the actual CPI values. How well does this model fit the data?

Fit the data with an additive seasonal regression model with second order polynomial trend component. (This process is described in section 11.20 of SMDA for quarterly data where p=4. In this case, the data is monthly so you’d need to use p=12.)
a. What is the equation of the model you estimated?

b. For each value in the sample data, plot the estimated CPI values produced by this model against the actual CPI values. How well does this model fit the data?

c. What is the value of the R2 statistic for this model? Interpret this value.

d. Forecast the next 12 CPI values (for Jan-Dec 2019) using this model.

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