For this exercise use data set ukcars , the quarterly UK passenger vehicle production data from 1977Q1–2005Q1. a. Plot the data and describe the main features of the series. b. Decompose the series...

For this exercise use data set ukcars , the quarterly UK passenger vehicle production data from 1977Q1–2005Q1.

a. Plot the data and describe the main features of the series.

b. Decompose the series using STL and obtain the seasonally adjusted data.

c. Forecast the next two years of the series using an additive damped trend method applied to the seasonally adjusted data. (This can be done in one step using stlf() with arguments etsmodel="AAN", damped=TRUE .)

d. Forecast the next two years of the series using Holt’s linear method applied to the seasonally adjusted data (as before but with damped=FALSE ).

e. Now use ets() to choose a seasonal model for the data.

f. Compare the RMSE of the ETS model with the RMSE of the models you obtained using STL decompositions. Which gives the better in-sample fits?

g. Compare the forecasts from the three approaches? Which seems most reasonable?

h. Check the residuals of your preferred model.