The purpose of this exam is to ensure you have developed a proper understanding of the theoretical (and practical) background to forecasting.
That is, you understand what a forecast is, the various types of forecast error measures, the importance of taking errors seriously when developing forecast models, methods to ensure robustness of forecasts in the context of structural change, and the value of a range of different types of forecast model.
Detail the steps involved in constructing a forecast using an ARIMA(1,1,1) model.
Compare forecast errors from using a first order autoregressive model, and a random walk model. Under which circumstances would you choose one over the other?
Show that forecast errors are non-decreasing in the forecast horizon.
Describe the Augmented Dickey-Fuller test. How important would you describe its role in the forecasting process?
Describe the Simple Exponential Smoothing (SES) method. What is the forecast equation?
Describe the Classical Time Series Decomposition approach. Is it recommended?
List the sources of forecast uncertainty; which would you suggest is most important, and why?
Compare and contrast the stepwise and general-to-specific model selection strategies.
Why is it not necessarily the case that the best possible in-sample model provides the best out-of-sample forecasts?
`Judgemental forecasts are always and everywhere a bad thing’. Evaluate this statement.
`The best forecast model is the most parsimonious one’. Evaluate this statement.
Describe impulse saturation; what benefits and costs do you see from the procedure?