1-Use augmented Dickey-Fuller tests to determine whether your chosen series is a unit
root process. Transform and re-test as appropriate to determine the order of
integration. ( 20 mark )

2-Define and estimate the ACF and PACF for your stationary time series (i.e. after
differencing your data if appropriate). (10 mark)

3-Select the most appropriate ARIMA time series model for your data on the basis of
ACF and PACF plots and appropriate experimentation. (40 mark)

4-Withhold ten per cent of the most recent data and re-estimate two of the equations
you estimated in (3). Using the re-estimated equations calculate forecasts (for the
withheld data) for each model using Excel. Judge which forecasts are ‘best’ using a
criterion such as RMSE (30 mark ).


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